Overview

Dataset statistics

Number of variables106
Number of observations1,112
Missing cells62,781
Missing cells (%)53.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory1.6 KiB

Variable types

Numeric76
Categorical30

Warnings

FILE_ID has a high cardinality: 1036 distinct values High cardinality
MEDICATION_NAME has a high cardinality: 101 distinct values High cardinality
Unnamed: 0 is highly correlated with Unnamed: 0.1 and 14 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
SUB_ID is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
X is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
subject is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
DX_GROUP is highly correlated with DSM_IV_TR and 28 other fieldsHigh correlation
DSM_IV_TR is highly correlated with DX_GROUP and 19 other fieldsHigh correlation
AGE_AT_SCAN is highly correlated with ADOS_MODULE and 6 other fieldsHigh correlation
SEX is highly correlated with SUB_IN_SMPHigh correlation
FIQ is highly correlated with VIQ and 14 other fieldsHigh correlation
VIQ is highly correlated with FIQ and 1 other fieldsHigh correlation
PIQ is highly correlated with FIQHigh correlation
ADI_R_SOCIAL_TOTAL_A is highly correlated with ADI_R_VERBAL_TOTAL_BV and 1 other fieldsHigh correlation
ADI_R_VERBAL_TOTAL_BV is highly correlated with ADI_R_SOCIAL_TOTAL_A and 1 other fieldsHigh correlation
ADI_R_ONSET_TOTAL_D is highly correlated with WISC_IV_BLK_DSN_SCALEDHigh correlation
ADI_R_RSRCH_RELIABLE is highly correlated with ADOS_RSRCH_RELIABLE and 1 other fieldsHigh correlation
ADOS_MODULE is highly correlated with AGE_AT_SCAN and 6 other fieldsHigh correlation
ADOS_TOTAL is highly correlated with DX_GROUP and 9 other fieldsHigh correlation
ADOS_COMM is highly correlated with DX_GROUP and 6 other fieldsHigh correlation
ADOS_SOCIAL is highly correlated with DX_GROUP and 11 other fieldsHigh correlation
ADOS_STEREO_BEHAV is highly correlated with ADOS_GOTHAM_RRB and 2 other fieldsHigh correlation
ADOS_RSRCH_RELIABLE is highly correlated with ADI_R_RSRCH_RELIABLEHigh correlation
ADOS_GOTHAM_SOCAFFECT is highly correlated with ADOS_TOTAL and 5 other fieldsHigh correlation
ADOS_GOTHAM_RRB is highly correlated with ADOS_STEREO_BEHAV and 2 other fieldsHigh correlation
ADOS_GOTHAM_TOTAL is highly correlated with ADOS_TOTAL and 7 other fieldsHigh correlation
ADOS_GOTHAM_SEVERITY is highly correlated with DX_GROUP and 10 other fieldsHigh correlation
SRS_VERSION is highly correlated with AGE_AT_SCAN and 4 other fieldsHigh correlation
SRS_RAW_TOTAL is highly correlated with DX_GROUP and 23 other fieldsHigh correlation
SRS_AWARENESS is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_COGNITION is highly correlated with DX_GROUP and 9 other fieldsHigh correlation
SRS_COMMUNICATION is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_MOTIVATION is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_MANNERISMS is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SCQ_TOTAL is highly correlated with DX_GROUP and 20 other fieldsHigh correlation
AQ_TOTAL is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
OFF_STIMULANTS_AT_SCAN is highly correlated with Unnamed: 0 and 10 other fieldsHigh correlation
VINELAND_RECEPTIVE_V_SCALED is highly correlated with Unnamed: 0 and 21 other fieldsHigh correlation
VINELAND_EXPRESSIVE_V_SCALED is highly correlated with Unnamed: 0 and 21 other fieldsHigh correlation
VINELAND_WRITTEN_V_SCALED is highly correlated with VINELAND_RECEPTIVE_V_SCALED and 10 other fieldsHigh correlation
VINELAND_COMMUNICATION_STANDARD is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
VINELAND_PERSONAL_V_SCALED is highly correlated with DX_GROUP and 12 other fieldsHigh correlation
VINELAND_DOMESTIC_V_SCALED is highly correlated with VINELAND_RECEPTIVE_V_SCALED and 12 other fieldsHigh correlation
VINELAND_COMMUNITY_V_SCALED is highly correlated with Unnamed: 0 and 25 other fieldsHigh correlation
VINELAND_DAILYLVNG_STANDARD is highly correlated with DX_GROUP and 16 other fieldsHigh correlation
VINELAND_INTERPERSONAL_V_SCALED is highly correlated with Unnamed: 0 and 21 other fieldsHigh correlation
VINELAND_PLAY_V_SCALED is highly correlated with Unnamed: 0 and 20 other fieldsHigh correlation
VINELAND_COPING_V_SCALED is highly correlated with Unnamed: 0 and 20 other fieldsHigh correlation
VINELAND_SOCIAL_STANDARD is highly correlated with Unnamed: 0 and 21 other fieldsHigh correlation
VINELAND_SUM_SCORES is highly correlated with Unnamed: 0 and 24 other fieldsHigh correlation
VINELAND_ABC_STANDARD is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
VINELAND_INFORMANT is highly correlated with AGE_AT_SCAN and 2 other fieldsHigh correlation
WISC_IV_VCI is highly correlated with FIQ and 6 other fieldsHigh correlation
WISC_IV_PRI is highly correlated with FIQ and 4 other fieldsHigh correlation
WISC_IV_WMI is highly correlated with FIQ and 4 other fieldsHigh correlation
WISC_IV_PSI is highly correlated with DX_GROUP and 7 other fieldsHigh correlation
WISC_IV_SIM_SCALED is highly correlated with FIQ and 4 other fieldsHigh correlation
WISC_IV_VOCAB_SCALED is highly correlated with FIQ and 6 other fieldsHigh correlation
WISC_IV_INFO_SCALED is highly correlated with FIQ and 6 other fieldsHigh correlation
WISC_IV_BLK_DSN_SCALED is highly correlated with ADI_R_ONSET_TOTAL_D and 2 other fieldsHigh correlation
WISC_IV_PIC_CON_SCALED is highly correlated with FIQ and 8 other fieldsHigh correlation
WISC_IV_MATRIX_SCALED is highly correlated with FIQ and 2 other fieldsHigh correlation
WISC_IV_DIGIT_SPAN_SCALED is highly correlated with FIQ and 7 other fieldsHigh correlation
WISC_IV_LET_NUM_SCALED is highly correlated with DX_GROUP and 4 other fieldsHigh correlation
WISC_IV_CODING_SCALED is highly correlated with DX_GROUP and 5 other fieldsHigh correlation
WISC_IV_SYM_SCALED is highly correlated with DX_GROUP and 9 other fieldsHigh correlation
AGE_AT_MPRAGE is highly correlated with AGE_AT_SCAN and 1 other fieldsHigh correlation
anat_efc is highly correlated with anat_snrHigh correlation
anat_snr is highly correlated with anat_efcHigh correlation
func_efc is highly correlated with OFF_STIMULANTS_AT_SCAN and 2 other fieldsHigh correlation
func_fber is highly correlated with OFF_STIMULANTS_AT_SCAN and 3 other fieldsHigh correlation
func_outlier is highly correlated with func_mean_fdHigh correlation
func_quality is highly correlated with func_mean_fd and 2 other fieldsHigh correlation
func_mean_fd is highly correlated with func_outlier and 3 other fieldsHigh correlation
func_num_fd is highly correlated with func_quality and 2 other fieldsHigh correlation
func_perc_fd is highly correlated with func_quality and 2 other fieldsHigh correlation
func_gsr is highly correlated with OFF_STIMULANTS_AT_SCAN and 2 other fieldsHigh correlation
SUB_IN_SMP is highly correlated with SEXHigh correlation
Unnamed: 0 is highly correlated with Unnamed: 0.1 and 14 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
SUB_ID is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
X is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
subject is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
DX_GROUP is highly correlated with DSM_IV_TR and 24 other fieldsHigh correlation
DSM_IV_TR is highly correlated with DX_GROUP and 22 other fieldsHigh correlation
AGE_AT_SCAN is highly correlated with ADOS_MODULE and 3 other fieldsHigh correlation
SEX is highly correlated with SUB_IN_SMPHigh correlation
FIQ is highly correlated with VIQ and 14 other fieldsHigh correlation
VIQ is highly correlated with FIQ and 1 other fieldsHigh correlation
PIQ is highly correlated with FIQHigh correlation
ADI_R_SOCIAL_TOTAL_A is highly correlated with ADI_R_VERBAL_TOTAL_BV and 1 other fieldsHigh correlation
ADI_R_VERBAL_TOTAL_BV is highly correlated with ADI_R_SOCIAL_TOTAL_A and 1 other fieldsHigh correlation
ADI_R_RSRCH_RELIABLE is highly correlated with ADOS_RSRCH_RELIABLE and 1 other fieldsHigh correlation
ADOS_MODULE is highly correlated with AGE_AT_SCAN and 6 other fieldsHigh correlation
ADOS_TOTAL is highly correlated with ADOS_COMM and 6 other fieldsHigh correlation
ADOS_COMM is highly correlated with ADOS_TOTAL and 4 other fieldsHigh correlation
ADOS_SOCIAL is highly correlated with ADOS_TOTAL and 6 other fieldsHigh correlation
ADOS_STEREO_BEHAV is highly correlated with ADOS_GOTHAM_RRB and 2 other fieldsHigh correlation
ADOS_RSRCH_RELIABLE is highly correlated with ADI_R_RSRCH_RELIABLEHigh correlation
ADOS_GOTHAM_SOCAFFECT is highly correlated with ADOS_TOTAL and 6 other fieldsHigh correlation
ADOS_GOTHAM_RRB is highly correlated with ADOS_STEREO_BEHAV and 2 other fieldsHigh correlation
ADOS_GOTHAM_TOTAL is highly correlated with ADOS_TOTAL and 7 other fieldsHigh correlation
ADOS_GOTHAM_SEVERITY is highly correlated with ADOS_TOTAL and 7 other fieldsHigh correlation
SRS_VERSION is highly correlated with AGE_AT_SCAN and 4 other fieldsHigh correlation
SRS_RAW_TOTAL is highly correlated with DX_GROUP and 21 other fieldsHigh correlation
SRS_AWARENESS is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_COGNITION is highly correlated with DX_GROUP and 9 other fieldsHigh correlation
SRS_COMMUNICATION is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_MOTIVATION is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_MANNERISMS is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SCQ_TOTAL is highly correlated with DX_GROUP and 20 other fieldsHigh correlation
AQ_TOTAL is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
OFF_STIMULANTS_AT_SCAN is highly correlated with Unnamed: 0 and 9 other fieldsHigh correlation
VINELAND_RECEPTIVE_V_SCALED is highly correlated with Unnamed: 0 and 21 other fieldsHigh correlation
VINELAND_EXPRESSIVE_V_SCALED is highly correlated with Unnamed: 0 and 21 other fieldsHigh correlation
VINELAND_WRITTEN_V_SCALED is highly correlated with DX_GROUP and 12 other fieldsHigh correlation
VINELAND_COMMUNICATION_STANDARD is highly correlated with Unnamed: 0 and 22 other fieldsHigh correlation
VINELAND_PERSONAL_V_SCALED is highly correlated with DX_GROUP and 12 other fieldsHigh correlation
VINELAND_DOMESTIC_V_SCALED is highly correlated with VINELAND_RECEPTIVE_V_SCALED and 9 other fieldsHigh correlation
VINELAND_COMMUNITY_V_SCALED is highly correlated with Unnamed: 0 and 24 other fieldsHigh correlation
VINELAND_DAILYLVNG_STANDARD is highly correlated with DX_GROUP and 17 other fieldsHigh correlation
VINELAND_INTERPERSONAL_V_SCALED is highly correlated with Unnamed: 0 and 20 other fieldsHigh correlation
VINELAND_PLAY_V_SCALED is highly correlated with Unnamed: 0 and 20 other fieldsHigh correlation
VINELAND_COPING_V_SCALED is highly correlated with Unnamed: 0 and 20 other fieldsHigh correlation
VINELAND_SOCIAL_STANDARD is highly correlated with Unnamed: 0 and 21 other fieldsHigh correlation
VINELAND_SUM_SCORES is highly correlated with Unnamed: 0 and 22 other fieldsHigh correlation
VINELAND_ABC_STANDARD is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
VINELAND_INFORMANT is highly correlated with ADI_R_RSRCH_RELIABLE and 1 other fieldsHigh correlation
WISC_IV_VCI is highly correlated with FIQ and 5 other fieldsHigh correlation
WISC_IV_PRI is highly correlated with FIQ and 5 other fieldsHigh correlation
WISC_IV_WMI is highly correlated with FIQ and 4 other fieldsHigh correlation
WISC_IV_PSI is highly correlated with DX_GROUP and 7 other fieldsHigh correlation
WISC_IV_SIM_SCALED is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_VOCAB_SCALED is highly correlated with FIQ and 4 other fieldsHigh correlation
WISC_IV_INFO_SCALED is highly correlated with FIQ and 6 other fieldsHigh correlation
WISC_IV_BLK_DSN_SCALED is highly correlated with WISC_IV_PRIHigh correlation
WISC_IV_PIC_CON_SCALED is highly correlated with FIQ and 7 other fieldsHigh correlation
WISC_IV_MATRIX_SCALED is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_DIGIT_SPAN_SCALED is highly correlated with FIQ and 6 other fieldsHigh correlation
WISC_IV_LET_NUM_SCALED is highly correlated with FIQ and 1 other fieldsHigh correlation
WISC_IV_CODING_SCALED is highly correlated with DX_GROUP and 6 other fieldsHigh correlation
WISC_IV_SYM_SCALED is highly correlated with DX_GROUP and 4 other fieldsHigh correlation
AGE_AT_MPRAGE is highly correlated with AGE_AT_SCAN and 1 other fieldsHigh correlation
BMI is highly correlated with AGE_AT_SCANHigh correlation
anat_cnr is highly correlated with anat_snrHigh correlation
anat_efc is highly correlated with anat_fberHigh correlation
anat_fber is highly correlated with anat_efcHigh correlation
anat_snr is highly correlated with anat_cnrHigh correlation
func_efc is highly correlated with func_fber and 1 other fieldsHigh correlation
func_fber is highly correlated with OFF_STIMULANTS_AT_SCAN and 3 other fieldsHigh correlation
func_outlier is highly correlated with SRS_VERSIONHigh correlation
func_quality is highly correlated with func_num_fd and 1 other fieldsHigh correlation
func_mean_fd is highly correlated with func_num_fd and 1 other fieldsHigh correlation
func_num_fd is highly correlated with func_quality and 2 other fieldsHigh correlation
func_perc_fd is highly correlated with func_quality and 2 other fieldsHigh correlation
func_gsr is highly correlated with OFF_STIMULANTS_AT_SCAN and 2 other fieldsHigh correlation
SUB_IN_SMP is highly correlated with SEXHigh correlation
Unnamed: 0 is highly correlated with Unnamed: 0.1 and 4 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
SUB_ID is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
X is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
subject is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
DX_GROUP is highly correlated with DSM_IV_TR and 19 other fieldsHigh correlation
DSM_IV_TR is highly correlated with DX_GROUP and 15 other fieldsHigh correlation
AGE_AT_SCAN is highly correlated with ADOS_MODULE and 2 other fieldsHigh correlation
SEX is highly correlated with SUB_IN_SMPHigh correlation
FIQ is highly correlated with VIQ and 11 other fieldsHigh correlation
VIQ is highly correlated with FIQHigh correlation
PIQ is highly correlated with FIQHigh correlation
ADI_R_SOCIAL_TOTAL_A is highly correlated with ADI_R_VERBAL_TOTAL_BVHigh correlation
ADI_R_VERBAL_TOTAL_BV is highly correlated with ADI_R_SOCIAL_TOTAL_AHigh correlation
ADI_R_RSRCH_RELIABLE is highly correlated with ADOS_RSRCH_RELIABLE and 1 other fieldsHigh correlation
ADOS_MODULE is highly correlated with AGE_AT_SCAN and 2 other fieldsHigh correlation
ADOS_TOTAL is highly correlated with ADOS_COMM and 4 other fieldsHigh correlation
ADOS_COMM is highly correlated with ADOS_TOTAL and 4 other fieldsHigh correlation
ADOS_SOCIAL is highly correlated with ADOS_TOTAL and 4 other fieldsHigh correlation
ADOS_STEREO_BEHAV is highly correlated with ADOS_GOTHAM_RRBHigh correlation
ADOS_RSRCH_RELIABLE is highly correlated with ADI_R_RSRCH_RELIABLEHigh correlation
ADOS_GOTHAM_SOCAFFECT is highly correlated with ADOS_TOTAL and 4 other fieldsHigh correlation
ADOS_GOTHAM_RRB is highly correlated with ADOS_STEREO_BEHAV and 2 other fieldsHigh correlation
ADOS_GOTHAM_TOTAL is highly correlated with ADOS_TOTAL and 5 other fieldsHigh correlation
ADOS_GOTHAM_SEVERITY is highly correlated with ADOS_TOTAL and 5 other fieldsHigh correlation
SRS_VERSION is highly correlated with AGE_AT_SCAN and 1 other fieldsHigh correlation
SRS_RAW_TOTAL is highly correlated with DX_GROUP and 15 other fieldsHigh correlation
SRS_AWARENESS is highly correlated with DX_GROUP and 7 other fieldsHigh correlation
SRS_COGNITION is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_COMMUNICATION is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_MOTIVATION is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SRS_MANNERISMS is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
SCQ_TOTAL is highly correlated with DX_GROUP and 15 other fieldsHigh correlation
AQ_TOTAL is highly correlated with DX_GROUP and 7 other fieldsHigh correlation
OFF_STIMULANTS_AT_SCAN is highly correlated with Unnamed: 0 and 9 other fieldsHigh correlation
VINELAND_RECEPTIVE_V_SCALED is highly correlated with DX_GROUP and 8 other fieldsHigh correlation
VINELAND_EXPRESSIVE_V_SCALED is highly correlated with DX_GROUP and 14 other fieldsHigh correlation
VINELAND_WRITTEN_V_SCALED is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 5 other fieldsHigh correlation
VINELAND_COMMUNICATION_STANDARD is highly correlated with DX_GROUP and 12 other fieldsHigh correlation
VINELAND_PERSONAL_V_SCALED is highly correlated with VINELAND_COMMUNITY_V_SCALED and 3 other fieldsHigh correlation
VINELAND_DOMESTIC_V_SCALED is highly correlated with VINELAND_DAILYLVNG_STANDARD and 2 other fieldsHigh correlation
VINELAND_COMMUNITY_V_SCALED is highly correlated with DX_GROUP and 15 other fieldsHigh correlation
VINELAND_DAILYLVNG_STANDARD is highly correlated with DX_GROUP and 11 other fieldsHigh correlation
VINELAND_INTERPERSONAL_V_SCALED is highly correlated with DX_GROUP and 13 other fieldsHigh correlation
VINELAND_PLAY_V_SCALED is highly correlated with DX_GROUP and 10 other fieldsHigh correlation
VINELAND_COPING_V_SCALED is highly correlated with DX_GROUP and 13 other fieldsHigh correlation
VINELAND_SOCIAL_STANDARD is highly correlated with DX_GROUP and 13 other fieldsHigh correlation
VINELAND_SUM_SCORES is highly correlated with DX_GROUP and 16 other fieldsHigh correlation
VINELAND_ABC_STANDARD is highly correlated with DX_GROUP and 16 other fieldsHigh correlation
VINELAND_INFORMANT is highly correlated with ADI_R_RSRCH_RELIABLE and 1 other fieldsHigh correlation
WISC_IV_VCI is highly correlated with FIQ and 4 other fieldsHigh correlation
WISC_IV_PRI is highly correlated with FIQ and 2 other fieldsHigh correlation
WISC_IV_WMI is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_PSI is highly correlated with FIQ and 2 other fieldsHigh correlation
WISC_IV_SIM_SCALED is highly correlated with WISC_IV_VCI and 1 other fieldsHigh correlation
WISC_IV_VOCAB_SCALED is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_INFO_SCALED is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_PIC_CON_SCALED is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_MATRIX_SCALED is highly correlated with FIQ and 1 other fieldsHigh correlation
WISC_IV_DIGIT_SPAN_SCALED is highly correlated with WISC_IV_WMIHigh correlation
WISC_IV_LET_NUM_SCALED is highly correlated with WISC_IV_WMIHigh correlation
WISC_IV_CODING_SCALED is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_SYM_SCALED is highly correlated with FIQ and 2 other fieldsHigh correlation
AGE_AT_MPRAGE is highly correlated with AGE_AT_SCAN and 1 other fieldsHigh correlation
anat_cnr is highly correlated with anat_snrHigh correlation
anat_efc is highly correlated with anat_fberHigh correlation
anat_fber is highly correlated with anat_efcHigh correlation
anat_snr is highly correlated with anat_cnrHigh correlation
func_efc is highly correlated with func_fberHigh correlation
func_fber is highly correlated with OFF_STIMULANTS_AT_SCAN and 2 other fieldsHigh correlation
func_mean_fd is highly correlated with func_num_fd and 1 other fieldsHigh correlation
func_num_fd is highly correlated with func_mean_fd and 1 other fieldsHigh correlation
func_perc_fd is highly correlated with func_mean_fd and 1 other fieldsHigh correlation
func_gsr is highly correlated with OFF_STIMULANTS_AT_SCANHigh correlation
SUB_IN_SMP is highly correlated with SEXHigh correlation
OFF_STIMULANTS_AT_SCAN is highly correlated with AQ_TOTAL and 26 other fieldsHigh correlation
AQ_TOTAL is highly correlated with OFF_STIMULANTS_AT_SCAN and 16 other fieldsHigh correlation
VINELAND_EXPRESSIVE_V_SCALED is highly correlated with X and 27 other fieldsHigh correlation
VINELAND_INFORMANT is highly correlated with SRS_VERSION and 3 other fieldsHigh correlation
X is highly correlated with OFF_STIMULANTS_AT_SCAN and 37 other fieldsHigh correlation
anat_efc is highly correlated with X and 12 other fieldsHigh correlation
qc_anat_rater_3 is highly correlated with WISC_IV_PIC_CON_SCALED and 5 other fieldsHigh correlation
anat_qi1 is highly correlated with OFF_STIMULANTS_AT_SCAN and 20 other fieldsHigh correlation
BMI is highly correlated with AQ_TOTAL and 15 other fieldsHigh correlation
SRS_AWARENESS is highly correlated with AQ_TOTAL and 12 other fieldsHigh correlation
ADOS_TOTAL is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 21 other fieldsHigh correlation
SRS_COMMUNICATION is highly correlated with AQ_TOTAL and 17 other fieldsHigh correlation
SCQ_TOTAL is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 28 other fieldsHigh correlation
VINELAND_SOCIAL_STANDARD is highly correlated with OFF_STIMULANTS_AT_SCAN and 25 other fieldsHigh correlation
anat_cnr is highly correlated with X and 14 other fieldsHigh correlation
WISC_IV_CODING_SCALED is highly correlated with WISC_IV_PIC_CON_SCALED and 12 other fieldsHigh correlation
VINELAND_PERSONAL_V_SCALED is highly correlated with OFF_STIMULANTS_AT_SCAN and 14 other fieldsHigh correlation
WISC_IV_PIC_CON_SCALED is highly correlated with qc_anat_rater_3 and 17 other fieldsHigh correlation
SRS_MANNERISMS is highly correlated with AQ_TOTAL and 11 other fieldsHigh correlation
WISC_IV_WMI is highly correlated with qc_func_notes_rater_3 and 17 other fieldsHigh correlation
SUB_IN_SMP is highly correlated with qc_func_notes_rater_3 and 7 other fieldsHigh correlation
VINELAND_PLAY_V_SCALED is highly correlated with OFF_STIMULANTS_AT_SCAN and 23 other fieldsHigh correlation
VINELAND_INTERPERSONAL_V_SCALED is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 19 other fieldsHigh correlation
ADOS_SOCIAL is highly correlated with ADOS_TOTAL and 21 other fieldsHigh correlation
WISC_IV_SIM_SCALED is highly correlated with ADOS_TOTAL and 11 other fieldsHigh correlation
qc_func_notes_rater_3 is highly correlated with OFF_STIMULANTS_AT_SCAN and 70 other fieldsHigh correlation
PIQ_TEST_TYPE is highly correlated with OFF_STIMULANTS_AT_SCAN and 27 other fieldsHigh correlation
anat_snr is highly correlated with anat_efcHigh correlation
SRS_MOTIVATION is highly correlated with AQ_TOTAL and 17 other fieldsHigh correlation
WISC_IV_VCI is highly correlated with WISC_IV_PIC_CON_SCALED and 19 other fieldsHigh correlation
ADOS_MODULE is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 21 other fieldsHigh correlation
anat_fwhm is highly correlated with X and 23 other fieldsHigh correlation
WISC_IV_VOCAB_SCALED is highly correlated with anat_qi1 and 20 other fieldsHigh correlation
anat_fber is highly correlated with anat_cnr and 2 other fieldsHigh correlation
SITE_ID is highly correlated with OFF_STIMULANTS_AT_SCAN and 37 other fieldsHigh correlation
WISC_IV_BLK_DSN_SCALED is highly correlated with WISC_IV_CODING_SCALED and 9 other fieldsHigh correlation
qc_anat_rater_2 is highly correlated with X and 13 other fieldsHigh correlation
func_mean_fd is highly correlated with BMI and 15 other fieldsHigh correlation
func_num_fd is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 13 other fieldsHigh correlation
SEX is highly correlated with SUB_IN_SMP and 2 other fieldsHigh correlation
SUB_ID is highly correlated with OFF_STIMULANTS_AT_SCAN and 34 other fieldsHigh correlation
VINELAND_SUM_SCORES is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 22 other fieldsHigh correlation
WISC_IV_LET_NUM_SCALED is highly correlated with anat_efc and 28 other fieldsHigh correlation
HANDEDNESS_SCORES is highly correlated with AQ_TOTAL and 5 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with OFF_STIMULANTS_AT_SCAN and 37 other fieldsHigh correlation
ADOS_COMM is highly correlated with AQ_TOTAL and 20 other fieldsHigh correlation
ADOS_GOTHAM_SOCAFFECT is highly correlated with ADOS_TOTAL and 16 other fieldsHigh correlation
SRS_VERSION is highly correlated with VINELAND_INFORMANT and 19 other fieldsHigh correlation
SRS_COGNITION is highly correlated with AQ_TOTAL and 16 other fieldsHigh correlation
ADI_RRB_TOTAL_C is highly correlated with AQ_TOTAL and 8 other fieldsHigh correlation
ADOS_GOTHAM_SEVERITY is highly correlated with ADOS_TOTAL and 14 other fieldsHigh correlation
ADI_R_ONSET_TOTAL_D is highly correlated with WISC_IV_SIM_SCALED and 5 other fieldsHigh correlation
qc_rater_1 is highly correlated with qc_func_notes_rater_3 and 10 other fieldsHigh correlation
AGE_AT_MPRAGE is highly correlated with anat_qi1 and 10 other fieldsHigh correlation
qc_func_rater_3 is highly correlated with qc_rater_1 and 3 other fieldsHigh correlation
WISC_IV_INFO_SCALED is highly correlated with WISC_IV_PIC_CON_SCALED and 17 other fieldsHigh correlation
DSM_IV_TR is highly correlated with OFF_STIMULANTS_AT_SCAN and 43 other fieldsHigh correlation
func_dvars is highly correlated with X and 9 other fieldsHigh correlation
FIQ is highly correlated with WISC_IV_CODING_SCALED and 15 other fieldsHigh correlation
CURRENT_MED_STATUS is highly correlated with OFF_STIMULANTS_AT_SCAN and 9 other fieldsHigh correlation
ADOS_GOTHAM_TOTAL is highly correlated with OFF_STIMULANTS_AT_SCAN and 20 other fieldsHigh correlation
func_gsr is highly correlated with OFF_STIMULANTS_AT_SCAN and 18 other fieldsHigh correlation
VINELAND_COMMUNITY_V_SCALED is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 21 other fieldsHigh correlation
ADI_R_RSRCH_RELIABLE is highly correlated with VINELAND_INFORMANT and 7 other fieldsHigh correlation
SRS_RAW_TOTAL is highly correlated with AQ_TOTAL and 29 other fieldsHigh correlation
func_fber is highly correlated with OFF_STIMULANTS_AT_SCAN and 18 other fieldsHigh correlation
qc_anat_notes_rater_2 is highly correlated with OFF_STIMULANTS_AT_SCAN and 34 other fieldsHigh correlation
ADOS_STEREO_BEHAV is highly correlated with ADOS_TOTAL and 13 other fieldsHigh correlation
func_efc is highly correlated with OFF_STIMULANTS_AT_SCAN and 29 other fieldsHigh correlation
WISC_IV_DIGIT_SPAN_SCALED is highly correlated with WISC_IV_CODING_SCALED and 21 other fieldsHigh correlation
VIQ_TEST_TYPE is highly correlated with OFF_STIMULANTS_AT_SCAN and 28 other fieldsHigh correlation
VIQ is highly correlated with SCQ_TOTAL and 5 other fieldsHigh correlation
PIQ is highly correlated with FIQ and 3 other fieldsHigh correlation
WISC_IV_SYM_SCALED is highly correlated with WISC_IV_PIC_CON_SCALED and 25 other fieldsHigh correlation
qc_func_rater_2 is highly correlated with X and 14 other fieldsHigh correlation
VINELAND_RECEPTIVE_V_SCALED is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 16 other fieldsHigh correlation
qc_func_notes_rater_2 is highly correlated with OFF_STIMULANTS_AT_SCAN and 42 other fieldsHigh correlation
FIQ_TEST_TYPE is highly correlated with OFF_STIMULANTS_AT_SCAN and 27 other fieldsHigh correlation
qc_anat_notes_rater_3 is highly correlated with X and 24 other fieldsHigh correlation
ADI_R_SOCIAL_TOTAL_A is highly correlated with AQ_TOTAL and 9 other fieldsHigh correlation
VINELAND_COMMUNICATION_STANDARD is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 22 other fieldsHigh correlation
AGE_AT_SCAN is highly correlated with OFF_STIMULANTS_AT_SCAN and 17 other fieldsHigh correlation
EYE_STATUS_AT_SCAN is highly correlated with X and 15 other fieldsHigh correlation
subject is highly correlated with OFF_STIMULANTS_AT_SCAN and 34 other fieldsHigh correlation
COMORBIDITY is highly correlated with AQ_TOTAL and 58 other fieldsHigh correlation
VINELAND_ABC_STANDARD is highly correlated with OFF_STIMULANTS_AT_SCAN and 21 other fieldsHigh correlation
qc_notes_rater_1 is highly correlated with X and 46 other fieldsHigh correlation
ADI_R_VERBAL_TOTAL_BV is highly correlated with AQ_TOTAL and 16 other fieldsHigh correlation
WISC_IV_MATRIX_SCALED is highly correlated with ADOS_TOTAL and 18 other fieldsHigh correlation
VINELAND_COPING_V_SCALED is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 19 other fieldsHigh correlation
func_quality is highly correlated with WISC_IV_WMI and 13 other fieldsHigh correlation
WISC_IV_PRI is highly correlated with WISC_IV_CODING_SCALED and 15 other fieldsHigh correlation
DX_GROUP is highly correlated with OFF_STIMULANTS_AT_SCAN and 42 other fieldsHigh correlation
func_outlier is highly correlated with WISC_IV_WMI and 7 other fieldsHigh correlation
VINELAND_WRITTEN_V_SCALED is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 17 other fieldsHigh correlation
ADOS_RSRCH_RELIABLE is highly correlated with X and 15 other fieldsHigh correlation
func_fwhm is highly correlated with OFF_STIMULANTS_AT_SCAN and 23 other fieldsHigh correlation
VINELAND_DOMESTIC_V_SCALED is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 16 other fieldsHigh correlation
func_perc_fd is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 14 other fieldsHigh correlation
WISC_IV_PSI is highly correlated with WISC_IV_CODING_SCALED and 21 other fieldsHigh correlation
Unnamed: 0 is highly correlated with OFF_STIMULANTS_AT_SCAN and 37 other fieldsHigh correlation
HANDEDNESS_CATEGORY is highly correlated with SRS_MANNERISMS and 2 other fieldsHigh correlation
ADOS_GOTHAM_RRB is highly correlated with X and 23 other fieldsHigh correlation
VINELAND_DAILYLVNG_STANDARD is highly correlated with VINELAND_EXPRESSIVE_V_SCALED and 22 other fieldsHigh correlation
DSM_IV_TR has 72 (6.5%) missing values Missing
HANDEDNESS_CATEGORY has 326 (29.3%) missing values Missing
HANDEDNESS_SCORES has 748 (67.3%) missing values Missing
FIQ has 72 (6.5%) missing values Missing
VIQ has 195 (17.5%) missing values Missing
PIQ has 181 (16.3%) missing values Missing
FIQ_TEST_TYPE has 169 (15.2%) missing values Missing
VIQ_TEST_TYPE has 278 (25.0%) missing values Missing
PIQ_TEST_TYPE has 259 (23.3%) missing values Missing
ADI_R_SOCIAL_TOTAL_A has 734 (66.0%) missing values Missing
ADI_R_VERBAL_TOTAL_BV has 733 (65.9%) missing values Missing
ADI_RRB_TOTAL_C has 734 (66.0%) missing values Missing
ADI_R_ONSET_TOTAL_D has 815 (73.3%) missing values Missing
ADI_R_RSRCH_RELIABLE has 721 (64.8%) missing values Missing
ADOS_MODULE has 605 (54.4%) missing values Missing
ADOS_TOTAL has 697 (62.7%) missing values Missing
ADOS_COMM has 722 (64.9%) missing values Missing
ADOS_SOCIAL has 721 (64.8%) missing values Missing
ADOS_STEREO_BEHAV has 781 (70.2%) missing values Missing
ADOS_RSRCH_RELIABLE has 736 (66.2%) missing values Missing
ADOS_GOTHAM_SOCAFFECT has 847 (76.2%) missing values Missing
ADOS_GOTHAM_RRB has 842 (75.7%) missing values Missing
ADOS_GOTHAM_TOTAL has 839 (75.4%) missing values Missing
ADOS_GOTHAM_SEVERITY has 839 (75.4%) missing values Missing
SRS_VERSION has 881 (79.2%) missing values Missing
SRS_RAW_TOTAL has 747 (67.2%) missing values Missing
SRS_AWARENESS has 1048 (94.2%) missing values Missing
SRS_COGNITION has 1048 (94.2%) missing values Missing
SRS_COMMUNICATION has 1048 (94.2%) missing values Missing
SRS_MOTIVATION has 1048 (94.2%) missing values Missing
SRS_MANNERISMS has 1048 (94.2%) missing values Missing
SCQ_TOTAL has 988 (88.8%) missing values Missing
AQ_TOTAL has 1056 (95.0%) missing values Missing
COMORBIDITY has 1041 (93.6%) missing values Missing
CURRENT_MED_STATUS has 302 (27.2%) missing values Missing
MEDICATION_NAME has 955 (85.9%) missing values Missing
OFF_STIMULANTS_AT_SCAN has 1040 (93.5%) missing values Missing
VINELAND_RECEPTIVE_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_EXPRESSIVE_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_WRITTEN_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_COMMUNICATION_STANDARD has 1000 (89.9%) missing values Missing
VINELAND_PERSONAL_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_DOMESTIC_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_COMMUNITY_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_DAILYLVNG_STANDARD has 1000 (89.9%) missing values Missing
VINELAND_INTERPERSONAL_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_PLAY_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_COPING_V_SCALED has 1000 (89.9%) missing values Missing
VINELAND_SOCIAL_STANDARD has 1000 (89.9%) missing values Missing
VINELAND_SUM_SCORES has 1000 (89.9%) missing values Missing
VINELAND_ABC_STANDARD has 1000 (89.9%) missing values Missing
VINELAND_INFORMANT has 1000 (89.9%) missing values Missing
WISC_IV_VCI has 1057 (95.1%) missing values Missing
WISC_IV_PRI has 1057 (95.1%) missing values Missing
WISC_IV_WMI has 1057 (95.1%) missing values Missing
WISC_IV_PSI has 1057 (95.1%) missing values Missing
WISC_IV_SIM_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_VOCAB_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_INFO_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_BLK_DSN_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_PIC_CON_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_MATRIX_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_DIGIT_SPAN_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_LET_NUM_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_CODING_SCALED has 1057 (95.1%) missing values Missing
WISC_IV_SYM_SCALED has 1057 (95.1%) missing values Missing
AGE_AT_MPRAGE has 1012 (91.0%) missing values Missing
BMI has 1018 (91.5%) missing values Missing
anat_cnr has 13 (1.2%) missing values Missing
anat_efc has 13 (1.2%) missing values Missing
anat_fber has 13 (1.2%) missing values Missing
anat_fwhm has 13 (1.2%) missing values Missing
anat_qi1 has 13 (1.2%) missing values Missing
anat_snr has 13 (1.2%) missing values Missing
func_efc has 13 (1.2%) missing values Missing
func_fber has 13 (1.2%) missing values Missing
func_fwhm has 13 (1.2%) missing values Missing
func_dvars has 13 (1.2%) missing values Missing
func_outlier has 13 (1.2%) missing values Missing
func_quality has 13 (1.2%) missing values Missing
func_mean_fd has 13 (1.2%) missing values Missing
func_num_fd has 13 (1.2%) missing values Missing
func_perc_fd has 13 (1.2%) missing values Missing
func_gsr has 13 (1.2%) missing values Missing
qc_notes_rater_1 has 1073 (96.5%) missing values Missing
qc_anat_rater_2 has 12 (1.1%) missing values Missing
qc_anat_notes_rater_2 has 837 (75.3%) missing values Missing
qc_func_rater_2 has 12 (1.1%) missing values Missing
qc_func_notes_rater_2 has 853 (76.7%) missing values Missing
qc_anat_notes_rater_3 has 1004 (90.3%) missing values Missing
qc_func_notes_rater_3 has 1036 (93.2%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0.1 is uniformly distributed Uniform
X is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Unnamed: 0.1 has unique values Unique
SUB_ID has unique values Unique
X has unique values Unique
subject has unique values Unique
ADOS_TOTAL has 13 (1.2%) zeros Zeros
ADOS_COMM has 22 (2.0%) zeros Zeros
ADOS_SOCIAL has 17 (1.5%) zeros Zeros
ADOS_STEREO_BEHAV has 77 (6.9%) zeros Zeros
ADOS_GOTHAM_RRB has 27 (2.4%) zeros Zeros
func_num_fd has 157 (14.1%) zeros Zeros
func_perc_fd has 157 (14.1%) zeros Zeros

Reproduction

Analysis started2021-06-01 22:42:12.191024
Analysis finished2021-06-01 22:42:39.276382
Duration27.09 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean555.5
Minimum0
Maximum1111
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:39.365274image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.55
Q1277.75
median555.5
Q3833.25
95-th percentile1055.45
Maximum1111
Range1111
Interquartile range (IQR)555.5

Descriptive statistics

Standard deviation321.1510548
Coefficient of variation (CV)0.5781297116
Kurtosis-1.2
Mean555.5
Median Absolute Deviation (MAD)278
Skewness0
Sum617716
Variance103138
MonotonicityStrictly increasing
2021-06-01T18:42:39.501672image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
7391
 
0.1%
7451
 
0.1%
7441
 
0.1%
7431
 
0.1%
7421
 
0.1%
7411
 
0.1%
7401
 
0.1%
7381
 
0.1%
7301
 
0.1%
Other values (1102)1102
99.1%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
11111
0.1%
11101
0.1%
11091
0.1%
11081
0.1%
11071
0.1%
11061
0.1%
11051
0.1%
11041
0.1%
11031
0.1%
11021
0.1%

Unnamed: 0.1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean556.5
Minimum1
Maximum1112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:39.649183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile56.55
Q1278.75
median556.5
Q3834.25
95-th percentile1056.45
Maximum1112
Range1111
Interquartile range (IQR)555.5

Descriptive statistics

Standard deviation321.1510548
Coefficient of variation (CV)0.5770908442
Kurtosis-1.2
Mean556.5
Median Absolute Deviation (MAD)278
Skewness0
Sum618828
Variance103138
MonotonicityStrictly increasing
2021-06-01T18:42:39.787745image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
7401
 
0.1%
7461
 
0.1%
7451
 
0.1%
7441
 
0.1%
7431
 
0.1%
7421
 
0.1%
7411
 
0.1%
7391
 
0.1%
7311
 
0.1%
Other values (1102)1102
99.1%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
11121
0.1%
11111
0.1%
11101
0.1%
11091
0.1%
11081
0.1%
11071
0.1%
11061
0.1%
11051
0.1%
11041
0.1%
11031
0.1%

SUB_ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50752.68435
Minimum50002
Maximum51607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:39.942900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum50002
5-th percentile50059.55
Q150354.75
median50724.5
Q351153.25
95-th percentile51469.45
Maximum51607
Range1605
Interquartile range (IQR)798.5

Descriptive statistics

Standard deviation447.6806659
Coefficient of variation (CV)0.008820827343
Kurtosis-1.257421407
Mean50752.68435
Median Absolute Deviation (MAD)398
Skewness0.06889714592
Sum56436985
Variance200417.9786
MonotonicityStrictly increasing
2021-06-01T18:42:40.338512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512011
 
0.1%
506501
 
0.1%
506561
 
0.1%
506551
 
0.1%
506541
 
0.1%
506531
 
0.1%
506521
 
0.1%
506511
 
0.1%
506491
 
0.1%
506281
 
0.1%
Other values (1102)1102
99.1%
ValueCountFrequency (%)
500021
0.1%
500031
0.1%
500041
0.1%
500051
0.1%
500061
0.1%
500071
0.1%
500081
0.1%
500091
0.1%
500101
0.1%
500111
0.1%
ValueCountFrequency (%)
516071
0.1%
516061
0.1%
515851
0.1%
515841
0.1%
515831
0.1%
515821
0.1%
515811
0.1%
515801
0.1%
515791
0.1%
515781
0.1%

X
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean556.5
Minimum1
Maximum1112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:40.497310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile56.55
Q1278.75
median556.5
Q3834.25
95-th percentile1056.45
Maximum1112
Range1111
Interquartile range (IQR)555.5

Descriptive statistics

Standard deviation321.1510548
Coefficient of variation (CV)0.5770908442
Kurtosis-1.2
Mean556.5
Median Absolute Deviation (MAD)278
Skewness0
Sum618828
Variance103138
MonotonicityStrictly increasing
2021-06-01T18:42:40.637764image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
7401
 
0.1%
7461
 
0.1%
7451
 
0.1%
7441
 
0.1%
7431
 
0.1%
7421
 
0.1%
7411
 
0.1%
7391
 
0.1%
7311
 
0.1%
Other values (1102)1102
99.1%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
11121
0.1%
11111
0.1%
11101
0.1%
11091
0.1%
11081
0.1%
11071
0.1%
11061
0.1%
11051
0.1%
11041
0.1%
11031
0.1%

subject
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50752.68435
Minimum50002
Maximum51607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:40.786517image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum50002
5-th percentile50059.55
Q150354.75
median50724.5
Q351153.25
95-th percentile51469.45
Maximum51607
Range1605
Interquartile range (IQR)798.5

Descriptive statistics

Standard deviation447.6806659
Coefficient of variation (CV)0.008820827343
Kurtosis-1.257421407
Mean50752.68435
Median Absolute Deviation (MAD)398
Skewness0.06889714592
Sum56436985
Variance200417.9786
MonotonicityStrictly increasing
2021-06-01T18:42:40.935542image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512011
 
0.1%
506501
 
0.1%
506561
 
0.1%
506551
 
0.1%
506541
 
0.1%
506531
 
0.1%
506521
 
0.1%
506511
 
0.1%
506491
 
0.1%
506281
 
0.1%
Other values (1102)1102
99.1%
ValueCountFrequency (%)
500021
0.1%
500031
0.1%
500041
0.1%
500051
0.1%
500061
0.1%
500071
0.1%
500081
0.1%
500091
0.1%
500101
0.1%
500111
0.1%
ValueCountFrequency (%)
516071
0.1%
516061
0.1%
515851
0.1%
515841
0.1%
515831
0.1%
515821
0.1%
515811
0.1%
515801
0.1%
515791
0.1%
515781
0.1%

SITE_ID
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
NYU
184 
UM_1
110 
USM
101 
UCLA_1
82 
PITT
 
57
Other values (15)
578 

Length

Max length8
Median length4
Mean length4.601618705
Min length3

Characters and Unicode

Total characters5,117
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPITT
2nd rowPITT
3rd rowPITT
4th rowPITT
5th rowPITT

Common Values

ValueCountFrequency (%)
NYU184
16.5%
UM_1110
 
9.9%
USM101
 
9.1%
UCLA_182
 
7.4%
PITT57
 
5.1%
MAX_MUN57
 
5.1%
YALE56
 
5.0%
KKI55
 
4.9%
TRINITY49
 
4.4%
STANFORD40
 
3.6%
Other values (10)321
28.9%

Length

2021-06-01T18:42:41.230637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nyu184
16.5%
um_1110
 
9.9%
usm101
 
9.1%
ucla_182
 
7.4%
pitt57
 
5.1%
max_mun57
 
5.1%
yale56
 
5.0%
kki55
 
4.9%
trinity49
 
4.4%
stanford40
 
3.6%
Other values (10)321
28.9%

Most occurring characters

ValueCountFrequency (%)
U751
14.7%
N430
 
8.4%
M387
 
7.6%
_375
 
7.3%
L333
 
6.5%
A300
 
5.9%
T290
 
5.7%
Y289
 
5.6%
S271
 
5.3%
I246
 
4.8%
Other values (14)1445
28.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4424
86.5%
Connector Punctuation375
 
7.3%
Decimal Number318
 
6.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U751
17.0%
N430
9.7%
M387
8.7%
L333
 
7.5%
A300
 
6.8%
T290
 
6.6%
Y289
 
6.5%
S271
 
6.1%
I246
 
5.6%
E222
 
5.0%
Other values (11)905
20.5%
Decimal Number
ValueCountFrequency (%)
1221
69.5%
297
30.5%
Connector Punctuation
ValueCountFrequency (%)
_375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4424
86.5%
Common693
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
U751
17.0%
N430
9.7%
M387
8.7%
L333
 
7.5%
A300
 
6.8%
T290
 
6.6%
Y289
 
6.5%
S271
 
6.1%
I246
 
5.6%
E222
 
5.0%
Other values (11)905
20.5%
Common
ValueCountFrequency (%)
_375
54.1%
1221
31.9%
297
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U751
14.7%
N430
 
8.4%
M387
 
7.6%
_375
 
7.3%
L333
 
6.5%
A300
 
5.9%
T290
 
5.7%
Y289
 
5.6%
S271
 
5.3%
I246
 
4.8%
Other values (14)1445
28.2%

FILE_ID
Categorical

HIGH CARDINALITY

Distinct1036
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size75.7 KiB
no_filename
 
77
Trinity_0050260
 
1
NYU_0051075
 
1
Yale_0050575
 
1
OHSU_0050163
 
1
Other values (1031)
1031 

Length

Max length16
Median length12
Mean length12.6205036
Min length11

Characters and Unicode

Total characters14,034
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1035 ?
Unique (%)93.1%

Sample

1st rowno_filename
2nd rowPitt_0050003
3rd rowPitt_0050004
4th rowPitt_0050005
5th rowPitt_0050006

Common Values

ValueCountFrequency (%)
no_filename77
 
6.9%
Trinity_00502601
 
0.1%
NYU_00510751
 
0.1%
Yale_00505751
 
0.1%
OHSU_00501631
 
0.1%
Stanford_00511711
 
0.1%
KKI_00507731
 
0.1%
Yale_00505761
 
0.1%
SBL_00515791
 
0.1%
NYU_00509521
 
0.1%
Other values (1026)1026
92.3%

Length

2021-06-01T18:42:41.521519image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no_filename77
 
6.9%
sbl_00515741
 
0.1%
leuven_2_00507451
 
0.1%
usm_00504831
 
0.1%
nyu_00510881
 
0.1%
sdsu_00502071
 
0.1%
caltech_00514611
 
0.1%
ucla_1_00512231
 
0.1%
yale_00506181
 
0.1%
stanford_00511631
 
0.1%
Other values (1026)1026
92.3%

Most occurring characters

ValueCountFrequency (%)
03065
21.8%
_1492
 
10.6%
51338
 
9.5%
1984
 
7.0%
U573
 
4.1%
2466
 
3.3%
n389
 
2.8%
e373
 
2.7%
3349
 
2.5%
M342
 
2.4%
Other values (35)4663
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7546
53.8%
Lowercase Letter2587
 
18.4%
Uppercase Letter2409
 
17.2%
Connector Punctuation1492
 
10.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n389
15.0%
e373
14.4%
a288
11.1%
i261
10.1%
t235
9.1%
l204
7.9%
o116
 
4.5%
f116
 
4.5%
u115
 
4.4%
r86
 
3.3%
Other values (8)404
15.6%
Uppercase Letter
ValueCountFrequency (%)
U573
23.8%
M342
14.2%
S238
9.9%
Y231
9.6%
L191
 
7.9%
N175
 
7.3%
C162
 
6.7%
A98
 
4.1%
K96
 
4.0%
O60
 
2.5%
Other values (6)243
10.1%
Decimal Number
ValueCountFrequency (%)
03065
40.6%
51338
17.7%
1984
 
13.0%
2466
 
6.2%
3349
 
4.6%
4303
 
4.0%
6303
 
4.0%
7280
 
3.7%
9239
 
3.2%
8219
 
2.9%
Connector Punctuation
ValueCountFrequency (%)
_1492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9038
64.4%
Latin4996
35.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
U573
 
11.5%
n389
 
7.8%
e373
 
7.5%
M342
 
6.8%
a288
 
5.8%
i261
 
5.2%
S238
 
4.8%
t235
 
4.7%
Y231
 
4.6%
l204
 
4.1%
Other values (24)1862
37.3%
Common
ValueCountFrequency (%)
03065
33.9%
_1492
16.5%
51338
14.8%
1984
 
10.9%
2466
 
5.2%
3349
 
3.9%
4303
 
3.4%
6303
 
3.4%
7280
 
3.1%
9239
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII14034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03065
21.8%
_1492
 
10.6%
51338
 
9.5%
1984
 
7.0%
U573
 
4.1%
2466
 
3.3%
n389
 
2.8%
e373
 
2.7%
3349
 
2.5%
M342
 
2.4%
Other values (35)4663
33.2%

DX_GROUP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.1 KiB
2
573 
1
539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,112
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2573
51.5%
1539
48.5%

Length

2021-06-01T18:42:41.745888image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:41.817924image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2573
51.5%
1539
48.5%

Most occurring characters

ValueCountFrequency (%)
2573
51.5%
1539
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1112
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2573
51.5%
1539
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common1112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2573
51.5%
1539
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2573
51.5%
1539
48.5%

DSM_IV_TR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.5%
Missing72
Missing (%)6.5%
Memory size63.9 KiB
0.0
558 
1.0
347 
2.0
93 
3.0
 
36
4.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3,120
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0558
50.2%
1.0347
31.2%
2.093
 
8.4%
3.036
 
3.2%
4.06
 
0.5%
(Missing)72
 
6.5%

Length

2021-06-01T18:42:42.002837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:42.083560image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0558
53.7%
1.0347
33.4%
2.093
 
8.9%
3.036
 
3.5%
4.06
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01598
51.2%
.1040
33.3%
1347
 
11.1%
293
 
3.0%
336
 
1.2%
46
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2080
66.7%
Other Punctuation1040
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01598
76.8%
1347
 
16.7%
293
 
4.5%
336
 
1.7%
46
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01598
51.2%
.1040
33.3%
1347
 
11.1%
293
 
3.0%
336
 
1.2%
46
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01598
51.2%
.1040
33.3%
1347
 
11.1%
293
 
3.0%
336
 
1.2%
46
 
0.2%

AGE_AT_SCAN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct709
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.04886421
Minimum6.47
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:42.186218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum6.47
5-th percentile8.58022
Q111.658575
median14.66
Q320.085
95-th percentile33
Maximum64
Range57.53
Interquartile range (IQR)8.426425

Descriptive statistics

Standard deviation8.036418919
Coefficient of variation (CV)0.471375619
Kurtosis4.002708979
Mean17.04886421
Median Absolute Deviation (MAD)3.66
Skewness1.755810429
Sum18958.337
Variance64.58402905
MonotonicityNot monotonic
2021-06-01T18:42:42.327287image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2113
 
1.2%
2212
 
1.1%
14.211
 
1.0%
1111
 
1.0%
2710
 
0.9%
199
 
0.8%
149
 
0.8%
209
 
0.8%
238
 
0.7%
318
 
0.7%
Other values (699)1012
91.0%
ValueCountFrequency (%)
6.471
 
0.1%
73
0.3%
7.131
 
0.1%
7.152
0.2%
7.191
 
0.1%
7.2281
 
0.1%
7.251
 
0.1%
7.261
 
0.1%
7.292
0.2%
7.52841
 
0.1%
ValueCountFrequency (%)
641
0.1%
581
0.1%
56.21
0.1%
55.41
0.1%
521
0.1%
50.22311
0.1%
491
0.1%
481
0.1%
462
0.2%
45.11
0.1%

SEX
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.1 KiB
1
948 
2
164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,112
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1948
85.3%
2164
 
14.7%

Length

2021-06-01T18:42:42.571148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:42.645137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1948
85.3%
2164
 
14.7%

Most occurring characters

ValueCountFrequency (%)
1948
85.3%
2164
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1112
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1948
85.3%
2164
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common1112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1948
85.3%
2164
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1948
85.3%
2164
 
14.7%

HANDEDNESS_CATEGORY
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.6%
Missing326
Missing (%)29.3%
Memory size54.9 KiB
R
693 
L
71 
Ambi
 
15
Mixed
 
6
L->R
 
1

Length

Max length5
Median length1
Mean length1.091603053
Min length1

Characters and Unicode

Total characters858
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAmbi
2nd rowR
3rd rowR
4th rowR
5th rowL

Common Values

ValueCountFrequency (%)
R693
62.3%
L71
 
6.4%
Ambi15
 
1.3%
Mixed6
 
0.5%
L->R1
 
0.1%
(Missing)326
29.3%

Length

2021-06-01T18:42:42.977033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:43.071480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
r693
88.2%
l71
 
9.0%
ambi15
 
1.9%
mixed6
 
0.8%
l->r1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R694
80.9%
L72
 
8.4%
i21
 
2.4%
A15
 
1.7%
m15
 
1.7%
b15
 
1.7%
M6
 
0.7%
x6
 
0.7%
e6
 
0.7%
d6
 
0.7%
Other values (2)2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter787
91.7%
Lowercase Letter69
 
8.0%
Dash Punctuation1
 
0.1%
Math Symbol1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i21
30.4%
m15
21.7%
b15
21.7%
x6
 
8.7%
e6
 
8.7%
d6
 
8.7%
Uppercase Letter
ValueCountFrequency (%)
R694
88.2%
L72
 
9.1%
A15
 
1.9%
M6
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%
Math Symbol
ValueCountFrequency (%)
>1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin856
99.8%
Common2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
R694
81.1%
L72
 
8.4%
i21
 
2.5%
A15
 
1.8%
m15
 
1.8%
b15
 
1.8%
M6
 
0.7%
x6
 
0.7%
e6
 
0.7%
d6
 
0.7%
Common
ValueCountFrequency (%)
-1
50.0%
>1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R694
80.9%
L72
 
8.4%
i21
 
2.4%
A15
 
1.7%
m15
 
1.7%
b15
 
1.7%
M6
 
0.7%
x6
 
0.7%
e6
 
0.7%
d6
 
0.7%
Other values (2)2
 
0.2%

HANDEDNESS_SCORES
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct105
Distinct (%)28.8%
Missing748
Missing (%)67.3%
Infinite0
Infinite (%)0.0%
Mean60.56722527
Minimum-100
Maximum100
Zeros1
Zeros (%)0.1%
Negative37
Negative (%)3.3%
Memory size8.8 KiB
2021-06-01T18:42:43.176529image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-65.6695
Q152.75
median75
Q391
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)38.25

Descriptive statistics

Standard deviation47.4958111
Coefficient of variation (CV)0.7841833745
Kurtosis3.15427408
Mean60.56722527
Median Absolute Deviation (MAD)20.5
Skewness-1.893137481
Sum22046.47
Variance2255.852072
MonotonicityNot monotonic
2021-06-01T18:42:43.326038image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10079
 
7.1%
8017
 
1.5%
9112
 
1.1%
9012
 
1.1%
7511
 
1.0%
8110
 
0.9%
8310
 
0.9%
93.338
 
0.7%
73.338
 
0.7%
608
 
0.7%
Other values (95)189
 
17.0%
(Missing)748
67.3%
ValueCountFrequency (%)
-1005
0.4%
-93.332
 
0.2%
-911
 
0.1%
-901
 
0.1%
-892
 
0.2%
-86.671
 
0.1%
-831
 
0.1%
-811
 
0.1%
-801
 
0.1%
-771
 
0.1%
ValueCountFrequency (%)
10079
7.1%
951
 
0.1%
941
 
0.1%
93.338
 
0.7%
9112
 
1.1%
9012
 
1.1%
892
 
0.2%
881
 
0.1%
86.675
 
0.4%
863
 
0.3%

FIQ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct123
Distinct (%)11.8%
Missing72
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean108.3809615
Minimum41
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:43.481958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile81
Q199
median109
Q3119
95-th percentile132
Maximum148
Range107
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.07206248
Coefficient of variation (CV)0.1390655911
Kurtosis0.108408166
Mean108.3809615
Median Absolute Deviation (MAD)10
Skewness-0.2373980777
Sum112716.2
Variance227.1670674
MonotonicityNot monotonic
2021-06-01T18:42:43.632756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10039
 
3.5%
10932
 
2.9%
11331
 
2.8%
11230
 
2.7%
10828
 
2.5%
11426
 
2.3%
11826
 
2.3%
10725
 
2.2%
11025
 
2.2%
10425
 
2.2%
Other values (113)753
67.7%
(Missing)72
 
6.5%
ValueCountFrequency (%)
411
 
0.1%
611
 
0.1%
641
 
0.1%
651
 
0.1%
691
 
0.1%
69.61
 
0.1%
711
 
0.1%
722
0.2%
733
0.3%
752
0.2%
ValueCountFrequency (%)
1483
0.3%
147.51
 
0.1%
146.51
 
0.1%
1461
 
0.1%
1441
 
0.1%
1423
0.3%
1413
0.3%
1401
 
0.1%
1393
0.3%
1382
0.2%

VIQ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct87
Distinct (%)9.5%
Missing195
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean107.8124318
Minimum42
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:43.778257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile81
Q198
median108
Q3119
95-th percentile132
Maximum180
Range138
Interquartile range (IQR)21

Descriptive statistics

Standard deviation16.24430496
Coefficient of variation (CV)0.1506719093
Kurtosis0.5659413978
Mean107.8124318
Median Absolute Deviation (MAD)11
Skewness-0.2314437166
Sum98864
Variance263.8774435
MonotonicityNot monotonic
2021-06-01T18:42:43.917776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10835
 
3.1%
10935
 
3.1%
11931
 
2.8%
10630
 
2.7%
9829
 
2.6%
9927
 
2.4%
11325
 
2.2%
11524
 
2.2%
11824
 
2.2%
12122
 
2.0%
Other values (77)635
57.1%
(Missing)195
 
17.5%
ValueCountFrequency (%)
421
0.1%
501
0.1%
552
0.2%
571
0.1%
591
0.1%
621
0.1%
661
0.1%
672
0.2%
692
0.2%
702
0.2%
ValueCountFrequency (%)
1801
 
0.1%
1491
 
0.1%
1472
0.2%
1451
 
0.1%
1441
 
0.1%
1433
0.3%
1413
0.3%
1403
0.3%
1394
0.4%
1382
0.2%

PIQ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct83
Distinct (%)8.9%
Missing181
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean106.6251343
Minimum37
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:44.073239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile81
Q197
median107
Q3117
95-th percentile129
Maximum157
Range120
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.33953259
Coefficient of variation (CV)0.1438641339
Kurtosis0.4617708415
Mean106.6251343
Median Absolute Deviation (MAD)10
Skewness-0.1955677098
Sum99268
Variance235.3012601
MonotonicityNot monotonic
2021-06-01T18:42:44.222820image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10648
 
4.3%
10339
 
3.5%
11939
 
3.5%
10937
 
3.3%
9932
 
2.9%
11530
 
2.7%
11128
 
2.5%
11026
 
2.3%
12026
 
2.3%
11725
 
2.2%
Other values (73)601
54.0%
(Missing)181
 
16.3%
ValueCountFrequency (%)
371
 
0.1%
591
 
0.1%
601
 
0.1%
631
 
0.1%
641
 
0.1%
661
 
0.1%
673
0.3%
701
 
0.1%
725
0.4%
732
 
0.2%
ValueCountFrequency (%)
1571
0.1%
1552
0.2%
1492
0.2%
1481
0.1%
1471
0.1%
1461
0.1%
1452
0.2%
1402
0.2%
1391
0.1%
1381
0.1%

FIQ_TEST_TYPE
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)1.3%
Missing169
Missing (%)15.2%
Memory size63.8 KiB
WASI
551 
WISC_IV_FULL
103 
WAIS_III
63 
DAS_II_SA
56 
WST
 
41
Other values (7)
129 

Length

Max length18
Median length4
Mean length6.377518558
Min length3

Characters and Unicode

Total characters6,014
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowWASI
2nd rowWASI
3rd rowWASI
4th rowWASI
5th rowWASI

Common Values

ValueCountFrequency (%)
WASI551
49.6%
WISC_IV_FULL103
 
9.3%
WAIS_III63
 
5.7%
DAS_II_SA56
 
5.0%
WST41
 
3.7%
WISC_III_DUTCH35
 
3.1%
WISC_IV_4_SUBTESTS33
 
3.0%
WISC28
 
2.5%
WISC_III15
 
1.3%
HAWIK_IV14
 
1.3%
Other values (2)4
 
0.4%
(Missing)169
 
15.2%

Length

2021-06-01T18:42:44.481203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wasi551
58.4%
wisc_iv_full103
 
10.9%
wais_iii63
 
6.7%
das_ii_sa56
 
5.9%
wst41
 
4.3%
wisc_iii_dutch35
 
3.7%
wisc_iv_4_subtests33
 
3.5%
wisc28
 
3.0%
wisc_iii15
 
1.6%
hawik_iv14
 
1.5%
Other values (2)4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
I1447
24.1%
S1083
18.0%
W886
14.7%
A743
12.4%
_579
9.6%
C249
 
4.1%
L206
 
3.4%
U171
 
2.8%
V150
 
2.5%
T143
 
2.4%
Other values (8)357
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5402
89.8%
Connector Punctuation579
 
9.6%
Decimal Number33
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I1447
26.8%
S1083
20.0%
W886
16.4%
A743
13.8%
C249
 
4.6%
L206
 
3.8%
U171
 
3.2%
V150
 
2.8%
T143
 
2.6%
F103
 
1.9%
Other values (6)221
 
4.1%
Connector Punctuation
ValueCountFrequency (%)
_579
100.0%
Decimal Number
ValueCountFrequency (%)
433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5402
89.8%
Common612
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
I1447
26.8%
S1083
20.0%
W886
16.4%
A743
13.8%
C249
 
4.6%
L206
 
3.8%
U171
 
3.2%
V150
 
2.8%
T143
 
2.6%
F103
 
1.9%
Other values (6)221
 
4.1%
Common
ValueCountFrequency (%)
_579
94.6%
433
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII6014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I1447
24.1%
S1083
18.0%
W886
14.7%
A743
12.4%
_579
9.6%
C249
 
4.1%
L206
 
3.4%
U171
 
2.8%
V150
 
2.5%
T143
 
2.4%
Other values (8)357
 
5.9%

VIQ_TEST_TYPE
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)1.4%
Missing278
Missing (%)25.0%
Memory size59.4 KiB
WASI
513 
PPVT
118 
DAS_II_SA
69 
WISC_IV_FULL
 
47
WAIS_III
 
43
Other values (7)
 
44

Length

Max length12
Median length4
Mean length5.071942446
Min length3

Characters and Unicode

Total characters4,230
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st rowWASI
2nd rowWASI
3rd rowWASI
4th rowWASI
5th rowWASI

Common Values

ValueCountFrequency (%)
WASI513
46.1%
PPVT118
 
10.6%
DAS_II_SA69
 
6.2%
WISC_IV_FULL47
 
4.2%
WAIS_III43
 
3.9%
WISC28
 
2.5%
GIT8
 
0.7%
WAIS3
 
0.3%
ppvt2
 
0.2%
WISC_III1
 
0.1%
Other values (2)2
 
0.2%
(Missing)278
25.0%

Length

2021-06-01T18:42:44.729258image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wasi513
61.5%
ppvt120
 
14.4%
das_ii_sa69
 
8.3%
wisc_iv_full47
 
5.6%
wais_iii43
 
5.2%
wisc28
 
3.4%
git8
 
1.0%
wais3
 
0.4%
wisc41
 
0.1%
wisc_iii1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I961
22.7%
S775
18.3%
A697
16.5%
W636
15.0%
_276
 
6.5%
P236
 
5.6%
V165
 
3.9%
T126
 
3.0%
L94
 
2.2%
C77
 
1.8%
Other values (14)187
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3938
93.1%
Connector Punctuation276
 
6.5%
Lowercase Letter15
 
0.4%
Decimal Number1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I961
24.4%
S775
19.7%
A697
17.7%
W636
16.2%
P236
 
6.0%
V165
 
4.2%
T126
 
3.2%
L94
 
2.4%
C77
 
2.0%
D69
 
1.8%
Other values (3)102
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
p4
26.7%
t3
20.0%
v2
13.3%
a1
 
6.7%
n1
 
6.7%
f1
 
6.7%
o1
 
6.7%
r1
 
6.7%
d1
 
6.7%
Connector Punctuation
ValueCountFrequency (%)
_276
100.0%
Decimal Number
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3953
93.5%
Common277
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
I961
24.3%
S775
19.6%
A697
17.6%
W636
16.1%
P236
 
6.0%
V165
 
4.2%
T126
 
3.2%
L94
 
2.4%
C77
 
1.9%
D69
 
1.7%
Other values (12)117
 
3.0%
Common
ValueCountFrequency (%)
_276
99.6%
41
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I961
22.7%
S775
18.3%
A697
16.5%
W636
15.0%
_276
 
6.5%
P236
 
5.6%
V165
 
3.9%
T126
 
3.0%
L94
 
2.2%
C77
 
1.8%
Other values (14)187
 
4.4%

PIQ_TEST_TYPE
Categorical

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)1.5%
Missing259
Missing (%)23.3%
Memory size60.2 KiB
WASI
513 
Ravens
114 
DAS_II_SA
70 
WISC_IV_FULL
 
47
WAIS_III
 
43
Other values (8)
66 

Length

Max length12
Median length4
Mean length5.377491208
Min length3

Characters and Unicode

Total characters4,587
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st rowWASI
2nd rowWASI
3rd rowWASI
4th rowWASI
5th rowWASI

Common Values

ValueCountFrequency (%)
WASI513
46.1%
Ravens114
 
10.3%
DAS_II_SA70
 
6.3%
WISC_IV_FULL47
 
4.2%
WAIS_III43
 
3.9%
WISC28
 
2.5%
RAVENS22
 
2.0%
GIT8
 
0.7%
WAIS3
 
0.3%
ravens2
 
0.2%
Other values (3)3
 
0.3%
(Missing)259
23.3%

Length

2021-06-01T18:42:44.964195image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wasi513
60.1%
ravens138
 
16.2%
das_ii_sa70
 
8.2%
wisc_iv_full47
 
5.5%
wais_iii43
 
5.0%
wisc28
 
3.3%
git8
 
0.9%
wais3
 
0.4%
wisc41
 
0.1%
wisc_iii1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I963
21.0%
S799
17.4%
A721
15.7%
W636
13.9%
_278
 
6.1%
R136
 
3.0%
a117
 
2.6%
n117
 
2.6%
v116
 
2.5%
e116
 
2.5%
Other values (17)588
12.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3719
81.1%
Lowercase Letter589
 
12.8%
Connector Punctuation278
 
6.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I963
25.9%
S799
21.5%
A721
19.4%
W636
17.1%
R136
 
3.7%
L94
 
2.5%
C77
 
2.1%
D70
 
1.9%
V69
 
1.9%
F47
 
1.3%
Other values (5)107
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
a117
19.9%
n117
19.9%
v116
19.7%
e116
19.7%
s116
19.7%
r3
 
0.5%
t1
 
0.2%
f1
 
0.2%
o1
 
0.2%
d1
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_278
100.0%
Decimal Number
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4308
93.9%
Common279
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I963
22.4%
S799
18.5%
A721
16.7%
W636
14.8%
R136
 
3.2%
a117
 
2.7%
n117
 
2.7%
v116
 
2.7%
e116
 
2.7%
s116
 
2.7%
Other values (15)471
10.9%
Common
ValueCountFrequency (%)
_278
99.6%
41
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I963
21.0%
S799
17.4%
A721
15.7%
W636
13.9%
_278
 
6.1%
R136
 
3.0%
a117
 
2.6%
n117
 
2.6%
v116
 
2.5%
e116
 
2.5%
Other values (17)588
12.8%

ADI_R_SOCIAL_TOTAL_A
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)6.9%
Missing734
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean19.76719577
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:45.077169image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q116
median20
Q324
95-th percentile27
Maximum30
Range28
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.527244609
Coefficient of variation (CV)0.2796170319
Kurtosis-0.4380435318
Mean19.76719577
Median Absolute Deviation (MAD)4
Skewness-0.4554920396
Sum7472
Variance30.55043296
MonotonicityNot monotonic
2021-06-01T18:42:45.189435image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1830
 
2.7%
2329
 
2.6%
2227
 
2.4%
2126
 
2.3%
2024
 
2.2%
2523
 
2.1%
2723
 
2.1%
2623
 
2.1%
2420
 
1.8%
1919
 
1.7%
Other values (16)134
 
12.1%
(Missing)734
66.0%
ValueCountFrequency (%)
21
 
0.1%
41
 
0.1%
72
 
0.2%
85
 
0.4%
96
0.5%
1014
1.3%
1110
0.9%
127
0.6%
1313
1.2%
1413
1.2%
ValueCountFrequency (%)
304
 
0.4%
294
 
0.4%
289
 
0.8%
2723
2.1%
2623
2.1%
2523
2.1%
2420
1.8%
2329
2.6%
2227
2.4%
2126
2.3%

ADI_R_VERBAL_TOTAL_BV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23
Distinct (%)6.1%
Missing733
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean15.79155673
Minimum0
Maximum26
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:45.308341image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q112
median16
Q319
95-th percentile23
Maximum26
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.63382234
Coefficient of variation (CV)0.2934367029
Kurtosis-0.2632844476
Mean15.79155673
Median Absolute Deviation (MAD)4
Skewness-0.2883039906
Sum5985
Variance21.47230947
MonotonicityNot monotonic
2021-06-01T18:42:45.540816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1530
 
2.7%
1730
 
2.7%
1629
 
2.6%
1829
 
2.6%
2028
 
2.5%
1928
 
2.5%
1225
 
2.2%
1324
 
2.2%
1123
 
2.1%
1419
 
1.7%
Other values (13)114
 
10.3%
(Missing)733
65.9%
ValueCountFrequency (%)
01
 
0.1%
22
 
0.2%
42
 
0.2%
63
 
0.3%
813
1.2%
915
1.3%
1016
1.4%
1123
2.1%
1225
2.2%
1324
2.2%
ValueCountFrequency (%)
261
 
0.1%
253
 
0.3%
247
 
0.6%
2314
1.3%
2219
1.7%
2118
1.6%
2028
2.5%
1928
2.5%
1829
2.6%
1730
2.7%

ADI_RRB_TOTAL_C
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)3.7%
Missing734
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean6.084656085
Minimum0
Maximum13
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:45.647160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile10
Maximum13
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.577854579
Coefficient of variation (CV)0.4236647961
Kurtosis-0.3492966471
Mean6.084656085
Median Absolute Deviation (MAD)2
Skewness0.1973047236
Sum2300
Variance6.645334232
MonotonicityNot monotonic
2021-06-01T18:42:45.761548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
672
 
6.5%
554
 
4.9%
845
 
4.0%
344
 
4.0%
737
 
3.3%
434
 
3.1%
1027
 
2.4%
924
 
2.2%
215
 
1.3%
1210
 
0.9%
Other values (4)16
 
1.4%
(Missing)734
66.0%
ValueCountFrequency (%)
03
 
0.3%
17
 
0.6%
215
 
1.3%
344
4.0%
434
3.1%
554
4.9%
672
6.5%
737
3.3%
845
4.0%
924
 
2.2%
ValueCountFrequency (%)
131
 
0.1%
1210
 
0.9%
115
 
0.4%
1027
 
2.4%
924
 
2.2%
845
4.0%
737
3.3%
672
6.5%
554
4.9%
434
3.1%

ADI_R_ONSET_TOTAL_D
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.0%
Missing815
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean3.218855219
Minimum0
Maximum5
Zeros6
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:45.863370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.261132067
Coefficient of variation (CV)0.3917952133
Kurtosis-0.3414035984
Mean3.218855219
Median Absolute Deviation (MAD)1
Skewness-0.2869333453
Sum956
Variance1.59045409
MonotonicityNot monotonic
2021-06-01T18:42:45.968419image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3121
 
10.9%
562
 
5.6%
446
 
4.1%
237
 
3.3%
125
 
2.2%
06
 
0.5%
(Missing)815
73.3%
ValueCountFrequency (%)
06
 
0.5%
125
 
2.2%
237
 
3.3%
3121
10.9%
446
 
4.1%
562
5.6%
ValueCountFrequency (%)
562
5.6%
446
 
4.1%
3121
10.9%
237
 
3.3%
125
 
2.2%
06
 
0.5%

ADI_R_RSRCH_RELIABLE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.5%
Missing721
Missing (%)64.8%
Memory size51.2 KiB
1.0
356 
0.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1,173
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0356
32.0%
0.035
 
3.1%
(Missing)721
64.8%

Length

2021-06-01T18:42:46.218941image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:46.293556image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0356
91.0%
0.035
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0426
36.3%
.391
33.3%
1356
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number782
66.7%
Other Punctuation391
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0426
54.5%
1356
45.5%
Other Punctuation
ValueCountFrequency (%)
.391
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0426
36.3%
.391
33.3%
1356
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0426
36.3%
.391
33.3%
1356
30.3%

ADOS_MODULE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.6%
Missing605
Missing (%)54.4%
Memory size53.5 KiB
3.0
322 
4.0
179 
2.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1,521
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0322
29.0%
4.0179
 
16.1%
2.06
 
0.5%
(Missing)605
54.4%

Length

2021-06-01T18:42:46.474139image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:46.550832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0322
63.5%
4.0179
35.3%
2.06
 
1.2%

Most occurring characters

ValueCountFrequency (%)
.507
33.3%
0507
33.3%
3322
21.2%
4179
 
11.8%
26
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1014
66.7%
Other Punctuation507
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0507
50.0%
3322
31.8%
4179
 
17.7%
26
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1521
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.507
33.3%
0507
33.3%
3322
21.2%
4179
 
11.8%
26
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.507
33.3%
0507
33.3%
3322
21.2%
4179
 
11.8%
26
 
0.4%

ADOS_TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct23
Distinct (%)5.5%
Missing697
Missing (%)62.7%
Infinite0
Infinite (%)0.0%
Mean11.0626506
Minimum0
Maximum22
Zeros13
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:46.631490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median11
Q314
95-th percentile18.3
Maximum22
Range22
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.63820802
Coefficient of variation (CV)0.4192673335
Kurtosis0.006941407999
Mean11.0626506
Median Absolute Deviation (MAD)3
Skewness-0.313157576
Sum4591
Variance21.51297363
MonotonicityNot monotonic
2021-06-01T18:42:46.743413image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1042
 
3.8%
1240
 
3.6%
1438
 
3.4%
935
 
3.1%
832
 
2.9%
1330
 
2.7%
1129
 
2.6%
728
 
2.5%
1521
 
1.9%
1719
 
1.7%
Other values (13)101
 
9.1%
(Missing)697
62.7%
ValueCountFrequency (%)
013
 
1.2%
17
 
0.6%
28
 
0.7%
32
 
0.2%
48
 
0.7%
56
 
0.5%
66
 
0.5%
728
2.5%
832
2.9%
935
3.1%
ValueCountFrequency (%)
221
 
0.1%
214
 
0.4%
206
 
0.5%
1910
 
0.9%
1811
 
1.0%
1719
1.7%
1619
1.7%
1521
1.9%
1438
3.4%
1330
2.7%

ADOS_COMM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)2.3%
Missing722
Missing (%)64.9%
Infinite0
Infinite (%)0.0%
Mean3.553846154
Minimum0
Maximum8
Zeros22
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:46.852371image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.75077598
Coefficient of variation (CV)0.4926425917
Kurtosis-0.4113198674
Mean3.553846154
Median Absolute Deviation (MAD)1
Skewness-0.07291004089
Sum1386
Variance3.065216532
MonotonicityNot monotonic
2021-06-01T18:42:46.959704image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
381
 
7.3%
579
 
7.1%
475
 
6.7%
261
 
5.5%
631
 
2.8%
125
 
2.2%
022
 
2.0%
713
 
1.2%
83
 
0.3%
(Missing)722
64.9%
ValueCountFrequency (%)
022
 
2.0%
125
 
2.2%
261
5.5%
381
7.3%
475
6.7%
579
7.1%
631
 
2.8%
713
 
1.2%
83
 
0.3%
ValueCountFrequency (%)
83
 
0.3%
713
 
1.2%
631
 
2.8%
579
7.1%
475
6.7%
381
7.3%
261
5.5%
125
 
2.2%
022
 
2.0%

ADOS_SOCIAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct15
Distinct (%)3.8%
Missing721
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean7.539641944
Minimum0
Maximum14
Zeros17
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:47.075394image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median8
Q310
95-th percentile13
Maximum14
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.337869561
Coefficient of variation (CV)0.4427092938
Kurtosis-0.2006914717
Mean7.539641944
Median Absolute Deviation (MAD)2
Skewness-0.3632120021
Sum2948
Variance11.1413732
MonotonicityNot monotonic
2021-06-01T18:42:47.177824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
753
 
4.8%
946
 
4.1%
643
 
3.9%
1043
 
3.9%
843
 
3.9%
530
 
2.7%
1127
 
2.4%
1318
 
1.6%
017
 
1.5%
1217
 
1.5%
Other values (5)54
 
4.9%
(Missing)721
64.8%
ValueCountFrequency (%)
017
 
1.5%
110
 
0.9%
29
 
0.8%
312
 
1.1%
414
 
1.3%
530
2.7%
643
3.9%
753
4.8%
843
3.9%
946
4.1%
ValueCountFrequency (%)
149
 
0.8%
1318
 
1.6%
1217
 
1.5%
1127
2.4%
1043
3.9%
946
4.1%
843
3.9%
753
4.8%
643
3.9%
530
2.7%

ADOS_STEREO_BEHAV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)2.7%
Missing781
Missing (%)70.2%
Infinite0
Infinite (%)0.0%
Mean1.921450151
Minimum0
Maximum8
Zeros77
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:47.288666image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.619911804
Coefficient of variation (CV)0.8430673071
Kurtosis1.049255971
Mean1.921450151
Median Absolute Deviation (MAD)1
Skewness0.9153150428
Sum636
Variance2.624114254
MonotonicityNot monotonic
2021-06-01T18:42:47.398348image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
285
 
7.6%
077
 
6.9%
163
 
5.7%
361
 
5.5%
424
 
2.2%
59
 
0.8%
67
 
0.6%
73
 
0.3%
82
 
0.2%
(Missing)781
70.2%
ValueCountFrequency (%)
077
6.9%
163
5.7%
285
7.6%
361
5.5%
424
 
2.2%
59
 
0.8%
67
 
0.6%
73
 
0.3%
82
 
0.2%
ValueCountFrequency (%)
82
 
0.2%
73
 
0.3%
67
 
0.6%
59
 
0.8%
424
 
2.2%
361
5.5%
285
7.6%
163
5.7%
077
6.9%

ADOS_RSRCH_RELIABLE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.5%
Missing736
Missing (%)66.2%
Memory size50.9 KiB
1.0
349 
0.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1,128
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0349
31.4%
0.027
 
2.4%
(Missing)736
66.2%

Length

2021-06-01T18:42:47.630421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:47.720017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0349
92.8%
0.027
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0403
35.7%
.376
33.3%
1349
30.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number752
66.7%
Other Punctuation376
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0403
53.6%
1349
46.4%
Other Punctuation
ValueCountFrequency (%)
.376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1128
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0403
35.7%
.376
33.3%
1349
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0403
35.7%
.376
33.3%
1349
30.9%

ADOS_GOTHAM_SOCAFFECT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)7.9%
Missing847
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean9.071698113
Minimum0
Maximum20
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:47.794725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median9
Q312
95-th percentile17
Maximum20
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.223687592
Coefficient of variation (CV)0.4655895225
Kurtosis-0.002173061494
Mean9.071698113
Median Absolute Deviation (MAD)3
Skewness0.1791055643
Sum2404
Variance17.83953688
MonotonicityNot monotonic
2021-06-01T18:42:47.913782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
830
 
2.7%
727
 
2.4%
1025
 
2.2%
1124
 
2.2%
623
 
2.1%
922
 
2.0%
517
 
1.5%
1217
 
1.5%
1316
 
1.4%
159
 
0.8%
Other values (11)55
 
4.9%
(Missing)847
76.2%
ValueCountFrequency (%)
08
 
0.7%
14
 
0.4%
26
 
0.5%
36
 
0.5%
45
 
0.4%
517
1.5%
623
2.1%
727
2.4%
830
2.7%
922
2.0%
ValueCountFrequency (%)
202
 
0.2%
193
 
0.3%
185
 
0.4%
176
 
0.5%
165
 
0.4%
159
 
0.8%
145
 
0.4%
1316
1.4%
1217
1.5%
1124
2.2%

ADOS_GOTHAM_RRB
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)3.3%
Missing842
Missing (%)75.7%
Infinite0
Infinite (%)0.0%
Mean2.840740741
Minimum0
Maximum8
Zeros27
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:48.033164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median3
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.857519483
Coefficient of variation (CV)0.653885607
Kurtosis0.1744651205
Mean2.840740741
Median Absolute Deviation (MAD)1
Skewness0.5852551881
Sum767
Variance3.450378631
MonotonicityNot monotonic
2021-06-01T18:42:48.137617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
363
 
5.7%
252
 
4.7%
445
 
4.0%
141
 
3.7%
027
 
2.4%
517
 
1.5%
613
 
1.2%
86
 
0.5%
76
 
0.5%
(Missing)842
75.7%
ValueCountFrequency (%)
027
2.4%
141
3.7%
252
4.7%
363
5.7%
445
4.0%
517
 
1.5%
613
 
1.2%
76
 
0.5%
86
 
0.5%
ValueCountFrequency (%)
86
 
0.5%
76
 
0.5%
613
 
1.2%
517
 
1.5%
445
4.0%
363
5.7%
252
4.7%
141
3.7%
027
2.4%

ADOS_GOTHAM_TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct29
Distinct (%)10.6%
Missing839
Missing (%)75.4%
Infinite0
Infinite (%)0.0%
Mean11.91208791
Minimum0
Maximum28
Zeros7
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:48.378537image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median12
Q315
95-th percentile21
Maximum28
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.32194752
Coefficient of variation (CV)0.4467686571
Kurtosis0.3395150821
Mean11.91208791
Median Absolute Deviation (MAD)3
Skewness0.1873244544
Sum3252
Variance28.3231254
MonotonicityNot monotonic
2021-06-01T18:42:48.488150image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1025
 
2.2%
1225
 
2.2%
924
 
2.2%
1321
 
1.9%
1420
 
1.8%
1118
 
1.6%
717
 
1.5%
1515
 
1.3%
1713
 
1.2%
813
 
1.2%
Other values (19)82
 
7.4%
(Missing)839
75.4%
ValueCountFrequency (%)
07
 
0.6%
14
 
0.4%
22
 
0.2%
35
 
0.4%
43
 
0.3%
55
 
0.4%
67
 
0.6%
717
1.5%
813
1.2%
924
2.2%
ValueCountFrequency (%)
281
 
0.1%
271
 
0.1%
262
 
0.2%
251
 
0.1%
242
 
0.2%
232
 
0.2%
222
 
0.2%
215
0.4%
207
0.6%
197
0.6%

ADOS_GOTHAM_SEVERITY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)3.7%
Missing839
Missing (%)75.4%
Infinite0
Infinite (%)0.0%
Mean6.758241758
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:48.600460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median7
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.486761977
Coefficient of variation (CV)0.3679599024
Kurtosis-0.08938498984
Mean6.758241758
Median Absolute Deviation (MAD)2
Skewness-0.7413416886
Sum1845
Variance6.183985133
MonotonicityNot monotonic
2021-06-01T18:42:48.695910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
649
 
4.4%
743
 
3.9%
941
 
3.7%
841
 
3.7%
1036
 
3.2%
118
 
1.6%
417
 
1.5%
513
 
1.2%
312
 
1.1%
23
 
0.3%
(Missing)839
75.4%
ValueCountFrequency (%)
118
 
1.6%
23
 
0.3%
312
 
1.1%
417
 
1.5%
513
 
1.2%
649
4.4%
743
3.9%
841
3.7%
941
3.7%
1036
3.2%
ValueCountFrequency (%)
1036
3.2%
941
3.7%
841
3.7%
743
3.9%
649
4.4%
513
 
1.2%
417
 
1.5%
312
 
1.1%
23
 
0.3%
118
 
1.6%

SRS_VERSION
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.9%
Missing881
Missing (%)79.2%
Memory size48.1 KiB
1.0
185 
2.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters693
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0185
 
16.6%
2.046
 
4.1%
(Missing)881
79.2%

Length

2021-06-01T18:42:48.915714image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:48.987895image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0185
80.1%
2.046
 
19.9%

Most occurring characters

ValueCountFrequency (%)
.231
33.3%
0231
33.3%
1185
26.7%
246
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number462
66.7%
Other Punctuation231
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0231
50.0%
1185
40.0%
246
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.231
33.3%
0231
33.3%
1185
26.7%
246
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.231
33.3%
0231
33.3%
1185
26.7%
246
 
6.6%

SRS_RAW_TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct129
Distinct (%)35.3%
Missing747
Missing (%)67.2%
Infinite0
Infinite (%)0.0%
Mean57.88219178
Minimum0
Maximum164
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:49.078694image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q119
median51
Q393
95-th percentile135.4
Maximum164
Range164
Interquartile range (IQR)74

Descriptive statistics

Standard deviation42.79904276
Coefficient of variation (CV)0.7394164154
Kurtosis-1.048135655
Mean57.88219178
Median Absolute Deviation (MAD)36
Skewness0.4147432575
Sum21127
Variance1831.758061
MonotonicityNot monotonic
2021-06-01T18:42:49.218890image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138
 
0.7%
98
 
0.7%
67
 
0.6%
1187
 
0.6%
196
 
0.5%
76
 
0.5%
236
 
0.5%
116
 
0.5%
1036
 
0.5%
176
 
0.5%
Other values (119)299
26.9%
(Missing)747
67.2%
ValueCountFrequency (%)
03
 
0.3%
13
 
0.3%
25
0.4%
32
 
0.2%
42
 
0.2%
55
0.4%
67
0.6%
76
0.5%
84
0.4%
98
0.7%
ValueCountFrequency (%)
1641
 
0.1%
1511
 
0.1%
1501
 
0.1%
1481
 
0.1%
1472
0.2%
1461
 
0.1%
1442
0.2%
1423
0.3%
1392
0.2%
1372
0.2%

SRS_AWARENESS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)23.4%
Missing1048
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean7.546875
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:49.344924image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14.75
median8
Q310.25
95-th percentile13
Maximum18
Range17
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.120909468
Coefficient of variation (CV)0.5460418342
Kurtosis-0.1430881407
Mean7.546875
Median Absolute Deviation (MAD)3
Skewness0.3323081661
Sum483
Variance16.98189484
MonotonicityNot monotonic
2021-06-01T18:42:49.452283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
88
 
0.7%
117
 
0.6%
96
 
0.5%
56
 
0.5%
15
 
0.4%
64
 
0.4%
104
 
0.4%
134
 
0.4%
24
 
0.4%
74
 
0.4%
Other values (5)12
 
1.1%
(Missing)1048
94.2%
ValueCountFrequency (%)
15
0.4%
24
0.4%
33
 
0.3%
44
0.4%
56
0.5%
64
0.4%
74
0.4%
88
0.7%
96
0.5%
104
0.4%
ValueCountFrequency (%)
182
 
0.2%
161
 
0.1%
134
0.4%
122
 
0.2%
117
0.6%
104
0.4%
96
0.5%
88
0.7%
74
0.4%
64
0.4%

SRS_COGNITION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)42.2%
Missing1048
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean10
Minimum0
Maximum29
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:49.571306image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q13.75
median7.5
Q315
95-th percentile23.85
Maximum29
Range29
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation7.690439334
Coefficient of variation (CV)0.7690439334
Kurtosis-0.4839485526
Mean10
Median Absolute Deviation (MAD)5.5
Skewness0.6618365083
Sum640
Variance59.14285714
MonotonicityNot monotonic
2021-06-01T18:42:49.692001image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
27
 
0.6%
76
 
0.5%
64
 
0.4%
114
 
0.4%
04
 
0.4%
143
 
0.3%
53
 
0.3%
183
 
0.3%
153
 
0.3%
103
 
0.3%
Other values (17)24
 
2.2%
(Missing)1048
94.2%
ValueCountFrequency (%)
04
0.4%
12
 
0.2%
27
0.6%
33
0.3%
43
0.3%
53
0.3%
64
0.4%
76
0.5%
81
 
0.1%
91
 
0.1%
ValueCountFrequency (%)
291
 
0.1%
271
 
0.1%
261
 
0.1%
241
 
0.1%
232
0.2%
221
 
0.1%
201
 
0.1%
192
0.2%
183
0.3%
171
 
0.1%

SRS_COMMUNICATION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)53.1%
Missing1048
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean17.40625
Minimum0
Maximum51
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:49.817332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median15
Q327
95-th percentile43.1
Maximum51
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.85291487
Coefficient of variation (CV)0.7384080356
Kurtosis-0.2864461776
Mean17.40625
Median Absolute Deviation (MAD)9
Skewness0.5845832585
Sum1114
Variance165.1974206
MonotonicityNot monotonic
2021-06-01T18:42:49.949900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
05
 
0.4%
34
 
0.4%
84
 
0.4%
93
 
0.3%
153
 
0.3%
103
 
0.3%
313
 
0.3%
243
 
0.3%
202
 
0.2%
442
 
0.2%
Other values (24)32
 
2.9%
(Missing)1048
94.2%
ValueCountFrequency (%)
05
0.4%
12
 
0.2%
21
 
0.1%
34
0.4%
41
 
0.1%
71
 
0.1%
84
0.4%
93
0.3%
103
0.3%
112
 
0.2%
ValueCountFrequency (%)
511
 
0.1%
461
 
0.1%
442
0.2%
381
 
0.1%
361
 
0.1%
341
 
0.1%
313
0.3%
301
 
0.1%
292
0.2%
282
0.2%

SRS_MOTIVATION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)42.2%
Missing1048
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean10.375
Minimum0
Maximum29
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:50.074885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.15
Q13.75
median8
Q316
95-th percentile23.85
Maximum29
Range29
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation7.541420017
Coefficient of variation (CV)0.7268838571
Kurtosis-0.7071954445
Mean10.375
Median Absolute Deviation (MAD)5
Skewness0.5532346755
Sum664
Variance56.87301587
MonotonicityNot monotonic
2021-06-01T18:42:50.194326image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
38
 
0.7%
64
 
0.4%
24
 
0.4%
134
 
0.4%
84
 
0.4%
123
 
0.3%
53
 
0.3%
73
 
0.3%
03
 
0.3%
43
 
0.3%
Other values (17)25
 
2.2%
(Missing)1048
94.2%
ValueCountFrequency (%)
03
 
0.3%
11
 
0.1%
24
0.4%
38
0.7%
43
 
0.3%
53
 
0.3%
64
0.4%
73
 
0.3%
84
0.4%
91
 
0.1%
ValueCountFrequency (%)
291
 
0.1%
261
 
0.1%
251
 
0.1%
241
 
0.1%
231
 
0.1%
212
0.2%
202
0.2%
193
0.3%
182
0.2%
171
 
0.1%

SRS_MANNERISMS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct22
Distinct (%)34.4%
Missing1048
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean8.9375
Minimum0
Maximum25
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:50.312276image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median7.5
Q315
95-th percentile21
Maximum25
Range25
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation7.028005204
Coefficient of variation (CV)0.7863502326
Kurtosis-0.8320568705
Mean8.9375
Median Absolute Deviation (MAD)5.5
Skewness0.476516563
Sum572
Variance49.39285714
MonotonicityNot monotonic
2021-06-01T18:42:50.428532image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
08
 
0.7%
167
 
0.6%
55
 
0.4%
25
 
0.4%
84
 
0.4%
64
 
0.4%
153
 
0.3%
13
 
0.3%
73
 
0.3%
43
 
0.3%
Other values (12)19
 
1.7%
(Missing)1048
94.2%
ValueCountFrequency (%)
08
0.7%
13
 
0.3%
25
0.4%
31
 
0.1%
43
 
0.3%
55
0.4%
64
0.4%
73
 
0.3%
84
0.4%
91
 
0.1%
ValueCountFrequency (%)
251
 
0.1%
232
 
0.2%
212
 
0.2%
202
 
0.2%
171
 
0.1%
167
0.6%
153
0.3%
142
 
0.2%
131
 
0.1%
122
 
0.2%

SCQ_TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33
Distinct (%)26.6%
Missing988
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean11.59677419
Minimum0
Maximum37
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:50.548588image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q319
95-th percentile27.85
Maximum37
Range37
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.602607501
Coefficient of variation (CV)0.8280412587
Kurtosis-0.8620110619
Mean11.59677419
Median Absolute Deviation (MAD)7
Skewness0.5068387114
Sum1438
Variance92.21007081
MonotonicityNot monotonic
2021-06-01T18:42:50.677407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
214
 
1.3%
112
 
1.1%
08
 
0.7%
37
 
0.6%
167
 
0.6%
96
 
0.5%
86
 
0.5%
155
 
0.4%
55
 
0.4%
65
 
0.4%
Other values (23)49
 
4.4%
(Missing)988
88.8%
ValueCountFrequency (%)
08
0.7%
112
1.1%
214
1.3%
37
0.6%
55
 
0.4%
65
 
0.4%
71
 
0.1%
86
0.5%
96
0.5%
101
 
0.1%
ValueCountFrequency (%)
371
 
0.1%
341
 
0.1%
311
 
0.1%
301
 
0.1%
292
0.2%
281
 
0.1%
272
0.2%
263
0.3%
253
0.3%
243
0.3%

AQ_TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)44.6%
Missing1056
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean21.91071429
Minimum7
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:50.936591image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.75
Q111.75
median20
Q332.25
95-th percentile39.25
Maximum42
Range35
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation11.04581663
Coefficient of variation (CV)0.5041285502
Kurtosis-1.389701874
Mean21.91071429
Median Absolute Deviation (MAD)10
Skewness0.263814946
Sum1227
Variance122.0100649
MonotonicityNot monotonic
2021-06-01T18:42:51.091644image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
335
 
0.4%
115
 
0.4%
174
 
0.4%
154
 
0.4%
124
 
0.4%
73
 
0.3%
363
 
0.3%
203
 
0.3%
102
 
0.2%
322
 
0.2%
Other values (15)21
 
1.9%
(Missing)1056
95.0%
ValueCountFrequency (%)
73
0.3%
82
 
0.2%
92
 
0.2%
102
 
0.2%
115
0.4%
124
0.4%
141
 
0.1%
154
0.4%
174
0.4%
203
0.3%
ValueCountFrequency (%)
421
 
0.1%
411
 
0.1%
401
 
0.1%
392
 
0.2%
371
 
0.1%
363
0.3%
335
0.4%
322
 
0.2%
312
 
0.2%
302
 
0.2%

COMORBIDITY
Categorical

HIGH CORRELATION
MISSING

Distinct39
Distinct (%)54.9%
Missing1041
Missing (%)93.6%
Memory size38.1 KiB
None
13 
ADHD Inattentive
10 
Mood Disorder NOS
ADHD Combined
Dysthymia
 
3
Other values (34)
37 

Length

Max length106
Median length16
Mean length22.1971831
Min length3

Characters and Unicode

Total characters1,576
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)43.7%

Sample

1st rowODD
2nd rowADHD (inattentive; present); ODD; MDE (past); Phobia (simple and social)
3rd rowADHD Combined Type; ODD; and Specific Phobia (bugs/thunderstorms)
4th rowADHD Combined and ODD
5th rowMDE (past); Dysthymic disorder

Common Values

ValueCountFrequency (%)
None13
 
1.2%
ADHD Inattentive10
 
0.9%
Mood Disorder NOS4
 
0.4%
ADHD Combined4
 
0.4%
Dysthymia3
 
0.3%
Generalized Anxiety Disorder2
 
0.2%
ADHD NOS2
 
0.2%
ODD2
 
0.2%
Specific Phobia: needles/shots1
 
0.1%
Anxiety Disorder NOS1
 
0.1%
Other values (29)29
 
2.6%
(Missing)1041
93.6%

Length

2021-06-01T18:42:51.386493image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adhd26
 
11.7%
disorder19
 
8.6%
nos16
 
7.2%
phobia14
 
6.3%
none13
 
5.9%
combined11
 
5.0%
inattentive11
 
5.0%
specific9
 
4.1%
anxiety9
 
4.1%
mood7
 
3.2%
Other values (54)87
39.2%

Most occurring characters

ValueCountFrequency (%)
152
 
9.6%
e136
 
8.6%
i133
 
8.4%
o96
 
6.1%
D94
 
6.0%
n93
 
5.9%
s77
 
4.9%
r75
 
4.8%
t70
 
4.4%
d62
 
3.9%
Other values (36)588
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1059
67.2%
Uppercase Letter303
 
19.2%
Space Separator152
 
9.6%
Other Punctuation47
 
3.0%
Open Punctuation7
 
0.4%
Close Punctuation7
 
0.4%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e136
12.8%
i133
12.6%
o96
9.1%
n93
8.8%
s77
 
7.3%
r75
 
7.1%
t70
 
6.6%
d62
 
5.9%
a60
 
5.7%
b34
 
3.2%
Other values (14)223
21.1%
Uppercase Letter
ValueCountFrequency (%)
D94
31.0%
A38
12.5%
N30
 
9.9%
S28
 
9.2%
H27
 
8.9%
O22
 
7.3%
P12
 
4.0%
C11
 
3.6%
I11
 
3.6%
M9
 
3.0%
Other values (4)21
 
6.9%
Other Punctuation
ValueCountFrequency (%)
;36
76.6%
:6
 
12.8%
/3
 
6.4%
&2
 
4.3%
Space Separator
ValueCountFrequency (%)
152
100.0%
Open Punctuation
ValueCountFrequency (%)
(7
100.0%
Close Punctuation
ValueCountFrequency (%)
)7
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1362
86.4%
Common214
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e136
 
10.0%
i133
 
9.8%
o96
 
7.0%
D94
 
6.9%
n93
 
6.8%
s77
 
5.7%
r75
 
5.5%
t70
 
5.1%
d62
 
4.6%
a60
 
4.4%
Other values (28)466
34.2%
Common
ValueCountFrequency (%)
152
71.0%
;36
 
16.8%
(7
 
3.3%
)7
 
3.3%
:6
 
2.8%
/3
 
1.4%
&2
 
0.9%
-1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
152
 
9.6%
e136
 
8.6%
i133
 
8.4%
o96
 
6.1%
D94
 
6.0%
n93
 
5.9%
s77
 
4.9%
r75
 
4.8%
t70
 
4.4%
d62
 
3.9%
Other values (36)588
37.3%

CURRENT_MED_STATUS
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.4%
Missing302
Missing (%)27.2%
Memory size55.4 KiB
0
673 
1
136 
`
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters810
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0673
60.5%
1136
 
12.2%
`1
 
0.1%
(Missing)302
27.2%

Length

2021-06-01T18:42:51.635884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:51.713614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0673
83.1%
1136
 
16.8%
1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0673
83.1%
1136
 
16.8%
`1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number809
99.9%
Modifier Symbol1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0673
83.2%
1136
 
16.8%
Modifier Symbol
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0673
83.1%
1136
 
16.8%
`1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0673
83.1%
1136
 
16.8%
`1
 
0.1%

MEDICATION_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct101
Distinct (%)64.3%
Missing955
Missing (%)85.9%
Memory size42.9 KiB
0
24 
Fluoxetine
 
6
Methylphenidate Extended Release
 
5
Lisdexamfetamine
 
4
Escitalopram
 
4
Other values (96)
114 

Length

Max length98
Median length23
Mean length27.25477707
Min length1

Characters and Unicode

Total characters4,279
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)52.9%

Sample

1st rowFluoxetine
2nd rowFluoxetine
3rd rowSertraline
4th rowPantoprazole
5th rowAtomoxetine

Common Values

ValueCountFrequency (%)
024
 
2.2%
Fluoxetine6
 
0.5%
Methylphenidate Extended Release5
 
0.4%
Lisdexamfetamine4
 
0.4%
Escitalopram4
 
0.4%
Sertraline4
 
0.4%
Atomoxetine4
 
0.4%
Risperidone3
 
0.3%
Dexmethylphenidate2
 
0.2%
Guanfacine Extended Release2
 
0.2%
Other values (91)99
 
8.9%
(Missing)955
85.9%

Length

2021-06-01T18:42:51.975772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
methylphenidate40
 
10.1%
extended31
 
7.8%
release31
 
7.8%
024
 
6.1%
risperidone22
 
5.6%
sertraline18
 
4.6%
amphetamine17
 
4.3%
and17
 
4.3%
dextroamphetamine16
 
4.1%
atomoxetine15
 
3.8%
Other values (65)164
41.5%

Most occurring characters

ValueCountFrequency (%)
e666
15.6%
a338
 
7.9%
i322
 
7.5%
t319
 
7.5%
n314
 
7.3%
239
 
5.6%
p194
 
4.5%
d193
 
4.5%
o182
 
4.3%
r181
 
4.2%
Other values (34)1331
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3559
83.2%
Uppercase Letter327
 
7.6%
Space Separator239
 
5.6%
Other Punctuation128
 
3.0%
Decimal Number26
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e666
18.7%
a338
9.5%
i322
9.0%
t319
9.0%
n314
8.8%
p194
 
5.5%
d193
 
5.4%
o182
 
5.1%
r181
 
5.1%
l175
 
4.9%
Other values (12)675
19.0%
Uppercase Letter
ValueCountFrequency (%)
M52
15.9%
R49
15.0%
A42
12.8%
E37
11.3%
D23
7.0%
S21
6.4%
C18
 
5.5%
L16
 
4.9%
F15
 
4.6%
G14
 
4.3%
Other values (8)40
12.2%
Decimal Number
ValueCountFrequency (%)
025
96.2%
11
 
3.8%
Other Punctuation
ValueCountFrequency (%)
;128
100.0%
Space Separator
ValueCountFrequency (%)
239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3886
90.8%
Common393
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e666
17.1%
a338
 
8.7%
i322
 
8.3%
t319
 
8.2%
n314
 
8.1%
p194
 
5.0%
d193
 
5.0%
o182
 
4.7%
r181
 
4.7%
l175
 
4.5%
Other values (30)1002
25.8%
Common
ValueCountFrequency (%)
239
60.8%
;128
32.6%
025
 
6.4%
11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e666
15.6%
a338
 
7.9%
i322
 
7.5%
t319
 
7.5%
n314
 
7.3%
239
 
5.6%
p194
 
4.5%
d193
 
4.5%
o182
 
4.3%
r181
 
4.2%
Other values (34)1331
31.1%

OFF_STIMULANTS_AT_SCAN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.8%
Missing1040
Missing (%)93.5%
Memory size45.0 KiB
0.0
48 
1.0
24 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters216
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.048
 
4.3%
1.024
 
2.2%
(Missing)1040
93.5%

Length

2021-06-01T18:42:52.209213image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:52.284204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.048
66.7%
1.024
33.3%

Most occurring characters

ValueCountFrequency (%)
0120
55.6%
.72
33.3%
124
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number144
66.7%
Other Punctuation72
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0120
83.3%
124
 
16.7%
Other Punctuation
ValueCountFrequency (%)
.72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0120
55.6%
.72
33.3%
124
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0120
55.6%
.72
33.3%
124
 
11.1%

VINELAND_RECEPTIVE_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)11.6%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean12.88392857
Minimum7
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:52.347887image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.55
Q110
median13
Q316
95-th percentile17.45
Maximum19
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.256996726
Coefficient of variation (CV)0.2527953106
Kurtosis-1.014535142
Mean12.88392857
Median Absolute Deviation (MAD)3
Skewness-0.1765001549
Sum1443
Variance10.60802767
MonotonicityNot monotonic
2021-06-01T18:42:52.459506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1617
 
1.5%
1317
 
1.5%
1515
 
1.3%
119
 
0.8%
109
 
0.8%
98
 
0.7%
88
 
0.7%
177
 
0.6%
126
 
0.5%
76
 
0.5%
Other values (3)10
 
0.9%
(Missing)1000
89.9%
ValueCountFrequency (%)
76
 
0.5%
88
0.7%
98
0.7%
109
0.8%
119
0.8%
126
 
0.5%
1317
1.5%
144
 
0.4%
1515
1.3%
1617
1.5%
ValueCountFrequency (%)
193
 
0.3%
183
 
0.3%
177
0.6%
1617
1.5%
1515
1.3%
144
 
0.4%
1317
1.5%
126
 
0.5%
119
0.8%
109
0.8%

VINELAND_EXPRESSIVE_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct18
Distinct (%)16.1%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean13.03571429
Minimum6
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:52.569450image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q110
median12.5
Q316
95-th percentile19
Maximum23
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.616599795
Coefficient of variation (CV)0.2774377925
Kurtosis-0.5277195865
Mean13.03571429
Median Absolute Deviation (MAD)2.5
Skewness0.3976013082
Sum1460
Variance13.07979408
MonotonicityNot monotonic
2021-06-01T18:42:52.681543image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1716
 
1.4%
1013
 
1.2%
913
 
1.2%
1212
 
1.1%
1311
 
1.0%
1110
 
0.9%
167
 
0.6%
157
 
0.6%
85
 
0.4%
145
 
0.4%
Other values (8)13
 
1.2%
(Missing)1000
89.9%
ValueCountFrequency (%)
61
 
0.1%
72
 
0.2%
85
 
0.4%
913
1.2%
1013
1.2%
1110
0.9%
1212
1.1%
1311
1.0%
145
 
0.4%
157
0.6%
ValueCountFrequency (%)
231
 
0.1%
221
 
0.1%
211
 
0.1%
201
 
0.1%
193
 
0.3%
183
 
0.3%
1716
1.4%
167
0.6%
157
0.6%
145
 
0.4%

VINELAND_WRITTEN_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)11.6%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean14.25
Minimum9
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:52.795203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile10
Q112
median14
Q316
95-th percentile20
Maximum21
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.927040753
Coefficient of variation (CV)0.2054063686
Kurtosis-0.4169360982
Mean14.25
Median Absolute Deviation (MAD)2
Skewness0.4224721022
Sum1596
Variance8.567567568
MonotonicityNot monotonic
2021-06-01T18:42:52.913931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1516
 
1.4%
1316
 
1.4%
1414
 
1.3%
1212
 
1.1%
1111
 
1.0%
1611
 
1.0%
107
 
0.6%
206
 
0.5%
195
 
0.4%
175
 
0.4%
Other values (3)9
 
0.8%
(Missing)1000
89.9%
ValueCountFrequency (%)
93
 
0.3%
107
0.6%
1111
1.0%
1212
1.1%
1316
1.4%
1414
1.3%
1516
1.4%
1611
1.0%
175
 
0.4%
184
 
0.4%
ValueCountFrequency (%)
212
 
0.2%
206
 
0.5%
195
 
0.4%
184
 
0.4%
175
 
0.4%
1611
1.0%
1516
1.4%
1414
1.3%
1316
1.4%
1212
1.1%

VINELAND_COMMUNICATION_STANDARD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct52
Distinct (%)46.4%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean90.00892857
Minimum40
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:53.044829image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile61.1
Q175
median89
Q3105
95-th percentile125
Maximum138
Range98
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.31397492
Coefficient of variation (CV)0.2256884428
Kurtosis-0.3638615395
Mean90.00892857
Median Absolute Deviation (MAD)15
Skewness0.02004242036
Sum10081
Variance412.6575772
MonotonicityNot monotonic
2021-06-01T18:42:53.191530image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
746
 
0.5%
776
 
0.5%
815
 
0.4%
824
 
0.4%
1064
 
0.4%
864
 
0.4%
1083
 
0.3%
1003
 
0.3%
753
 
0.3%
1053
 
0.3%
Other values (42)71
 
6.4%
(Missing)1000
89.9%
ValueCountFrequency (%)
401
 
0.1%
441
 
0.1%
481
 
0.1%
512
0.2%
601
 
0.1%
623
0.3%
642
0.2%
672
0.2%
682
0.2%
691
 
0.1%
ValueCountFrequency (%)
1381
 
0.1%
1341
 
0.1%
1273
0.3%
1252
0.2%
1201
 
0.1%
1181
 
0.1%
1172
0.2%
1162
0.2%
1153
0.3%
1132
0.2%

VINELAND_PERSONAL_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)13.4%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean13.99107143
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:53.311628image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9
Q112
median14
Q316
95-th percentile19
Maximum23
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.123567883
Coefficient of variation (CV)0.2232543733
Kurtosis-0.3261182779
Mean13.99107143
Median Absolute Deviation (MAD)2
Skewness0.04274296803
Sum1567
Variance9.756676319
MonotonicityNot monotonic
2021-06-01T18:42:53.418688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1614
 
1.3%
1513
 
1.2%
1413
 
1.2%
1312
 
1.1%
1710
 
0.9%
1110
 
0.9%
129
 
0.8%
108
 
0.7%
187
 
0.6%
95
 
0.4%
Other values (5)11
 
1.0%
(Missing)1000
89.9%
ValueCountFrequency (%)
71
 
0.1%
83
 
0.3%
95
 
0.4%
108
0.7%
1110
0.9%
129
0.8%
1312
1.1%
1413
1.2%
1513
1.2%
1614
1.3%
ValueCountFrequency (%)
231
 
0.1%
203
 
0.3%
193
 
0.3%
187
0.6%
1710
0.9%
1614
1.3%
1513
1.2%
1413
1.2%
1312
1.1%
129
0.8%

VINELAND_DOMESTIC_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)15.2%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:53.659595image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7.55
Q111
median13
Q315
95-th percentile17
Maximum20
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.25770051
Coefficient of variation (CV)0.2606160408
Kurtosis1.490889849
Mean12.5
Median Absolute Deviation (MAD)2
Skewness-0.7696913751
Sum1400
Variance10.61261261
MonotonicityNot monotonic
2021-06-01T18:42:53.766423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1115
 
1.3%
1314
 
1.3%
1513
 
1.2%
1213
 
1.2%
1412
 
1.1%
1012
 
1.1%
179
 
0.8%
168
 
0.7%
97
 
0.6%
22
 
0.2%
Other values (7)7
 
0.6%
(Missing)1000
89.9%
ValueCountFrequency (%)
22
 
0.2%
31
 
0.1%
41
 
0.1%
51
 
0.1%
71
 
0.1%
81
 
0.1%
97
0.6%
1012
1.1%
1115
1.3%
1213
1.2%
ValueCountFrequency (%)
201
 
0.1%
191
 
0.1%
179
0.8%
168
0.7%
1513
1.2%
1412
1.1%
1314
1.3%
1213
1.2%
1115
1.3%
1012
1.1%

VINELAND_COMMUNITY_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)13.4%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean14.58035714
Minimum4
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:53.882837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10
Q112
median14.5
Q318
95-th percentile19
Maximum21
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3440505
Coefficient of variation (CV)0.2293531267
Kurtosis-0.3446017854
Mean14.58035714
Median Absolute Deviation (MAD)2.5
Skewness-0.1942823958
Sum1633
Variance11.18267375
MonotonicityNot monotonic
2021-06-01T18:42:53.992661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1416
 
1.4%
1114
 
1.3%
1614
 
1.3%
1813
 
1.2%
1911
 
1.0%
1510
 
0.9%
107
 
0.6%
127
 
0.6%
137
 
0.6%
93
 
0.3%
Other values (5)10
 
0.9%
(Missing)1000
89.9%
ValueCountFrequency (%)
41
 
0.1%
81
 
0.1%
93
 
0.3%
107
0.6%
1114
1.3%
127
0.6%
137
0.6%
1416
1.4%
1510
0.9%
1614
1.3%
ValueCountFrequency (%)
213
 
0.3%
202
 
0.2%
1911
1.0%
1813
1.2%
173
 
0.3%
1614
1.3%
1510
0.9%
1416
1.4%
137
0.6%
127
0.6%

VINELAND_DAILYLVNG_STANDARD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct50
Distinct (%)44.6%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean91.44642857
Minimum42
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:54.133772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile66.65
Q179
median91
Q3104.25
95-th percentile119.9
Maximum127
Range85
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation17.15684891
Coefficient of variation (CV)0.1876163912
Kurtosis-0.1308307332
Mean91.44642857
Median Absolute Deviation (MAD)13
Skewness-0.1599212209
Sum10242
Variance294.3574646
MonotonicityNot monotonic
2021-06-01T18:42:54.277051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
897
 
0.6%
956
 
0.5%
765
 
0.4%
1125
 
0.4%
815
 
0.4%
854
 
0.4%
1054
 
0.4%
914
 
0.4%
874
 
0.4%
1074
 
0.4%
Other values (40)64
 
5.8%
(Missing)1000
89.9%
ValueCountFrequency (%)
421
 
0.1%
522
0.2%
571
 
0.1%
611
 
0.1%
651
 
0.1%
683
0.3%
691
 
0.1%
711
 
0.1%
721
 
0.1%
732
0.2%
ValueCountFrequency (%)
1271
0.1%
1261
0.1%
1251
0.1%
1241
0.1%
1231
0.1%
1211
0.1%
1191
0.1%
1161
0.1%
1141
0.1%
1132
0.2%

VINELAND_INTERPERSONAL_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)15.2%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean11.92857143
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:54.403015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.55
Q19
median11.5
Q315
95-th percentile18
Maximum20
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.98807747
Coefficient of variation (CV)0.3343298478
Kurtosis-0.4288586156
Mean11.92857143
Median Absolute Deviation (MAD)3.5
Skewness-0.1668348685
Sum1336
Variance15.9047619
MonotonicityNot monotonic
2021-06-01T18:42:54.511094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1015
 
1.3%
1512
 
1.1%
1610
 
0.9%
810
 
0.9%
119
 
0.8%
98
 
0.7%
78
 
0.7%
127
 
0.6%
187
 
0.6%
147
 
0.6%
Other values (7)19
 
1.7%
(Missing)1000
89.9%
ValueCountFrequency (%)
12
 
0.2%
53
 
0.3%
61
 
0.1%
78
0.7%
810
0.9%
98
0.7%
1015
1.3%
119
0.8%
127
0.6%
135
 
0.4%
ValueCountFrequency (%)
201
 
0.1%
192
 
0.2%
187
0.6%
175
0.4%
1610
0.9%
1512
1.1%
147
0.6%
135
0.4%
127
0.6%
119
0.8%

VINELAND_PLAY_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)15.2%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean12.61607143
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:54.626090image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q110
median13
Q316
95-th percentile18
Maximum19
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.020769358
Coefficient of variation (CV)0.3187021713
Kurtosis-0.03535747265
Mean12.61607143
Median Absolute Deviation (MAD)3
Skewness-0.5212515718
Sum1413
Variance16.16658623
MonotonicityNot monotonic
2021-06-01T18:42:54.732657image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1613
 
1.2%
1311
 
1.0%
1410
 
0.9%
1710
 
0.9%
1010
 
0.9%
129
 
0.8%
119
 
0.8%
88
 
0.7%
188
 
0.7%
76
 
0.5%
Other values (7)18
 
1.6%
(Missing)1000
89.9%
ValueCountFrequency (%)
12
 
0.2%
31
 
0.1%
41
 
0.1%
51
 
0.1%
76
0.5%
88
0.7%
95
0.4%
1010
0.9%
119
0.8%
129
0.8%
ValueCountFrequency (%)
194
 
0.4%
188
0.7%
1710
0.9%
1613
1.2%
154
 
0.4%
1410
0.9%
1311
1.0%
129
0.8%
119
0.8%
1010
0.9%

VINELAND_COPING_V_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)15.2%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean14.79464286
Minimum6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:54.845955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9
Q112
median14.5
Q318
95-th percentile20.45
Maximum22
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.78507832
Coefficient of variation (CV)0.2558411417
Kurtosis-0.9220307115
Mean14.79464286
Median Absolute Deviation (MAD)3.5
Skewness-0.03388220191
Sum1657
Variance14.32681789
MonotonicityNot monotonic
2021-06-01T18:42:54.956953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1212
 
1.1%
1311
 
1.0%
1110
 
0.9%
1910
 
0.9%
189
 
0.8%
149
 
0.8%
179
 
0.8%
208
 
0.7%
167
 
0.6%
157
 
0.6%
Other values (7)20
 
1.8%
(Missing)1000
89.9%
ValueCountFrequency (%)
61
 
0.1%
71
 
0.1%
82
 
0.2%
95
0.4%
105
0.4%
1110
0.9%
1212
1.1%
1311
1.0%
149
0.8%
157
0.6%
ValueCountFrequency (%)
222
 
0.2%
214
 
0.4%
208
0.7%
1910
0.9%
189
0.8%
179
0.8%
167
0.6%
157
0.6%
149
0.8%
1311
1.0%

VINELAND_SOCIAL_STANDARD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct61
Distinct (%)54.5%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean89.08928571
Minimum20
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:55.091430image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile54.55
Q175
median88
Q3106.25
95-th percentile124
Maximum138
Range118
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation23.03329105
Coefficient of variation (CV)0.2585416514
Kurtosis0.1194461193
Mean89.08928571
Median Absolute Deviation (MAD)15
Skewness-0.2542132654
Sum9978
Variance530.5324968
MonotonicityNot monotonic
2021-06-01T18:42:55.238005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
788
 
0.7%
825
 
0.4%
694
 
0.4%
914
 
0.4%
1184
 
0.4%
804
 
0.4%
1073
 
0.3%
1033
 
0.3%
833
 
0.3%
753
 
0.3%
Other values (51)71
 
6.4%
(Missing)1000
89.9%
ValueCountFrequency (%)
201
0.1%
231
0.1%
381
0.1%
481
0.1%
501
0.1%
541
0.1%
551
0.1%
561
0.1%
612
0.2%
632
0.2%
ValueCountFrequency (%)
1381
0.1%
1321
0.1%
1291
0.1%
1281
0.1%
1261
0.1%
1242
0.2%
1231
0.1%
1222
0.2%
1212
0.2%
1192
0.2%

VINELAND_SUM_SCORES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct85
Distinct (%)75.9%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean272.0357143
Minimum128
Maximum469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:55.382043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile180.75
Q1228.75
median269.5
Q3308
95-th percentile357
Maximum469
Range341
Interquartile range (IQR)79.25

Descriptive statistics

Standard deviation58.1310927
Coefficient of variation (CV)0.2136891946
Kurtosis0.4503259588
Mean272.0357143
Median Absolute Deviation (MAD)39.5
Skewness0.1196925392
Sum30468
Variance3379.223938
MonotonicityNot monotonic
2021-06-01T18:42:55.521061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2594
 
0.4%
3083
 
0.3%
2813
 
0.3%
2283
 
0.3%
2603
 
0.3%
3482
 
0.2%
2692
 
0.2%
3572
 
0.2%
2802
 
0.2%
1962
 
0.2%
Other values (75)86
 
7.7%
(Missing)1000
89.9%
ValueCountFrequency (%)
1281
0.1%
1361
0.1%
1381
0.1%
1741
0.1%
1771
0.1%
1781
0.1%
1831
0.1%
1841
0.1%
1951
0.1%
1962
0.2%
ValueCountFrequency (%)
4691
0.1%
3881
0.1%
3791
0.1%
3691
0.1%
3651
0.1%
3572
0.2%
3562
0.2%
3521
0.1%
3482
0.2%
3371
0.1%

VINELAND_ABC_STANDARD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct56
Distinct (%)50.0%
Missing1000
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean89.1875
Minimum41
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:55.672862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile59.55
Q174
median88
Q3102.25
95-th percentile122.45
Maximum131
Range90
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation19.83923279
Coefficient of variation (CV)0.2224440957
Kurtosis-0.4856875513
Mean89.1875
Median Absolute Deviation (MAD)14
Skewness0.02417908569
Sum9989
Variance393.5951577
MonotonicityNot monotonic
2021-06-01T18:42:55.815197image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
925
 
0.4%
735
 
0.4%
884
 
0.4%
954
 
0.4%
1014
 
0.4%
814
 
0.4%
834
 
0.4%
844
 
0.4%
873
 
0.3%
1233
 
0.3%
Other values (46)72
 
6.5%
(Missing)1000
89.9%
ValueCountFrequency (%)
411
0.1%
441
0.1%
451
0.1%
571
0.1%
581
0.1%
591
0.1%
602
0.2%
632
0.2%
642
0.2%
651
0.1%
ValueCountFrequency (%)
1311
 
0.1%
1281
 
0.1%
1271
 
0.1%
1233
0.3%
1221
 
0.1%
1212
0.2%
1193
0.3%
1151
 
0.1%
1142
0.2%
1131
 
0.1%

VINELAND_INFORMANT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.8%
Missing1000
Missing (%)89.9%
Memory size45.8 KiB
1.0
108 
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters336
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0108
 
9.7%
2.04
 
0.4%
(Missing)1000
89.9%

Length

2021-06-01T18:42:56.047984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:56.241716image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0108
96.4%
2.04
 
3.6%

Most occurring characters

ValueCountFrequency (%)
.112
33.3%
0112
33.3%
1108
32.1%
24
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number224
66.7%
Other Punctuation112
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112
50.0%
1108
48.2%
24
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.112
33.3%
0112
33.3%
1108
32.1%
24
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.112
33.3%
0112
33.3%
1108
32.1%
24
 
1.2%

WISC_IV_VCI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)43.6%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean112.1090909
Minimum63
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:56.318033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile84.6
Q1100
median116
Q3125
95-th percentile134.6
Maximum144
Range81
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.1428343
Coefficient of variation (CV)0.1529120802
Kurtosis0.03358291745
Mean112.1090909
Median Absolute Deviation (MAD)12
Skewness-0.5608697169
Sum6166
Variance293.8767677
MonotonicityNot monotonic
2021-06-01T18:42:56.438384image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1217
 
0.6%
1167
 
0.6%
1045
 
0.4%
1124
 
0.4%
1284
 
0.4%
1303
 
0.3%
1002
 
0.2%
892
 
0.2%
992
 
0.2%
872
 
0.2%
Other values (14)17
 
1.5%
(Missing)1057
95.1%
ValueCountFrequency (%)
631
 
0.1%
792
 
0.2%
872
 
0.2%
892
 
0.2%
912
 
0.2%
961
 
0.1%
981
 
0.1%
992
 
0.2%
1002
 
0.2%
1045
0.4%
ValueCountFrequency (%)
1441
 
0.1%
1401
 
0.1%
1361
 
0.1%
1341
 
0.1%
1322
 
0.2%
1303
0.3%
1284
0.4%
1261
 
0.1%
1241
 
0.1%
1217
0.6%

WISC_IV_PRI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct22
Distinct (%)40.0%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean106.6181818
Minimum79
Maximum137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:56.561891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile85.4
Q197
median106
Q3116
95-th percentile129.4
Maximum137
Range58
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.96754294
Coefficient of variation (CV)0.1310052629
Kurtosis-0.4102522523
Mean106.6181818
Median Absolute Deviation (MAD)10
Skewness0.1638074665
Sum5864
Variance195.0922559
MonotonicityNot monotonic
2021-06-01T18:42:56.671808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
926
 
0.5%
1025
 
0.4%
1154
 
0.4%
1083
 
0.3%
1213
 
0.3%
1043
 
0.3%
1173
 
0.3%
1273
 
0.3%
983
 
0.3%
1123
 
0.3%
Other values (12)19
 
1.7%
(Missing)1057
95.1%
ValueCountFrequency (%)
792
 
0.2%
841
 
0.1%
862
 
0.2%
926
0.5%
941
 
0.1%
962
 
0.2%
983
0.3%
1002
 
0.2%
1025
0.4%
1043
0.3%
ValueCountFrequency (%)
1371
 
0.1%
1352
0.2%
1273
0.3%
1231
 
0.1%
1213
0.3%
1191
 
0.1%
1173
0.3%
1154
0.4%
1123
0.3%
1102
0.2%

WISC_IV_WMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)36.4%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean100.6909091
Minimum56
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:56.787974image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile78.2
Q192.5
median102
Q3110
95-th percentile123
Maximum126
Range70
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation15.14359995
Coefficient of variation (CV)0.1503968937
Kurtosis0.8794273725
Mean100.6909091
Median Absolute Deviation (MAD)8
Skewness-0.7258734986
Sum5538
Variance229.3286195
MonotonicityNot monotonic
2021-06-01T18:42:56.897044image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1078
 
0.7%
945
 
0.4%
974
 
0.4%
1134
 
0.4%
914
 
0.4%
1203
 
0.3%
803
 
0.3%
1023
 
0.3%
1103
 
0.3%
1043
 
0.3%
Other values (10)15
 
1.3%
(Missing)1057
95.1%
ValueCountFrequency (%)
561
 
0.1%
591
 
0.1%
741
 
0.1%
803
0.3%
831
 
0.1%
862
 
0.2%
881
 
0.1%
914
0.4%
945
0.4%
974
0.4%
ValueCountFrequency (%)
1262
 
0.2%
1232
 
0.2%
1203
 
0.3%
1162
 
0.2%
1134
0.4%
1103
 
0.3%
1078
0.7%
1043
 
0.3%
1023
 
0.3%
992
 
0.2%

WISC_IV_PSI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)38.2%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean96.69090909
Minimum68
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:57.012727image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile74.4
Q184
median100
Q3107.5
95-th percentile115.9
Maximum131
Range63
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14.30989392
Coefficient of variation (CV)0.1479962703
Kurtosis-0.6179365656
Mean96.69090909
Median Absolute Deviation (MAD)9
Skewness-0.02627098406
Sum5318
Variance204.773064
MonotonicityNot monotonic
2021-06-01T18:42:57.120384image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1036
 
0.5%
1096
 
0.5%
835
 
0.4%
944
 
0.4%
1064
 
0.4%
1004
 
0.4%
853
 
0.3%
1153
 
0.3%
783
 
0.3%
913
 
0.3%
Other values (11)14
 
1.3%
(Missing)1057
95.1%
ValueCountFrequency (%)
681
 
0.1%
701
 
0.1%
731
 
0.1%
751
 
0.1%
783
0.3%
802
 
0.2%
835
0.4%
853
0.3%
881
 
0.1%
913
0.3%
ValueCountFrequency (%)
1311
 
0.1%
1231
 
0.1%
1181
 
0.1%
1153
0.3%
1122
 
0.2%
1096
0.5%
1064
0.4%
1036
0.5%
1004
0.4%
972
 
0.2%

WISC_IV_SIM_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)21.8%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean12.38181818
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:57.228331image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.1
Q111
median13
Q314.5
95-th percentile16
Maximum17
Range16
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.094145661
Coefficient of variation (CV)0.2498942898
Kurtosis2.719826013
Mean12.38181818
Median Absolute Deviation (MAD)2
Skewness-1.29114908
Sum681
Variance9.573737374
MonotonicityNot monotonic
2021-06-01T18:42:57.326650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1315
 
1.3%
169
 
0.8%
127
 
0.6%
105
 
0.4%
154
 
0.4%
114
 
0.4%
143
 
0.3%
92
 
0.2%
82
 
0.2%
52
 
0.2%
Other values (2)2
 
0.2%
(Missing)1057
95.1%
ValueCountFrequency (%)
11
 
0.1%
52
 
0.2%
82
 
0.2%
92
 
0.2%
105
 
0.4%
114
 
0.4%
127
0.6%
1315
1.3%
143
 
0.3%
154
 
0.4%
ValueCountFrequency (%)
171
 
0.1%
169
0.8%
154
 
0.4%
143
 
0.3%
1315
1.3%
127
0.6%
114
 
0.4%
105
 
0.4%
92
 
0.2%
82
 
0.2%

WISC_IV_VOCAB_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)27.3%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean11.81818182
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:57.435014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.4
Q19.5
median13
Q314
95-th percentile17
Maximum18
Range17
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.646870343
Coefficient of variation (CV)0.3085813367
Kurtosis0.7049356954
Mean11.81818182
Median Absolute Deviation (MAD)2
Skewness-0.8779886244
Sum650
Variance13.2996633
MonotonicityNot monotonic
2021-06-01T18:42:57.530201image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
139
 
0.8%
158
 
0.7%
128
 
0.7%
148
 
0.7%
94
 
0.4%
103
 
0.3%
83
 
0.3%
172
 
0.2%
62
 
0.2%
72
 
0.2%
Other values (5)6
 
0.5%
(Missing)1057
95.1%
ValueCountFrequency (%)
11
 
0.1%
31
 
0.1%
41
 
0.1%
62
 
0.2%
72
 
0.2%
83
 
0.3%
94
0.4%
103
 
0.3%
111
 
0.1%
128
0.7%
ValueCountFrequency (%)
182
 
0.2%
172
 
0.2%
158
0.7%
148
0.7%
139
0.8%
128
0.7%
111
 
0.1%
103
 
0.3%
94
0.4%
83
 
0.3%

WISC_IV_INFO_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)23.6%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean12.12727273
Minimum5
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:57.634122image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q110
median13
Q315
95-th percentile16
Maximum17
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.103489944
Coefficient of variation (CV)0.2559099654
Kurtosis-0.5557862917
Mean12.12727273
Median Absolute Deviation (MAD)2
Skewness-0.5436991047
Sum667
Variance9.631649832
MonotonicityNot monotonic
2021-06-01T18:42:57.738510image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
159
 
0.8%
148
 
0.7%
106
 
0.5%
136
 
0.5%
115
 
0.4%
94
 
0.4%
164
 
0.4%
124
 
0.4%
63
 
0.3%
172
 
0.2%
Other values (3)4
 
0.4%
(Missing)1057
95.1%
ValueCountFrequency (%)
51
 
0.1%
63
 
0.3%
72
 
0.2%
81
 
0.1%
94
0.4%
106
0.5%
115
0.4%
124
0.4%
136
0.5%
148
0.7%
ValueCountFrequency (%)
172
 
0.2%
164
0.4%
159
0.8%
148
0.7%
136
0.5%
124
0.4%
115
0.4%
106
0.5%
94
0.4%
81
 
0.1%

WISC_IV_BLK_DSN_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct14
Distinct (%)25.5%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean11.63636364
Minimum4
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:57.836651image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q110
median11
Q313
95-th percentile17.3
Maximum19
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.01455504
Coefficient of variation (CV)0.2590633237
Kurtosis0.7629419068
Mean11.63636364
Median Absolute Deviation (MAD)2
Skewness0.3717797512
Sum640
Variance9.087542088
MonotonicityNot monotonic
2021-06-01T18:42:57.936984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1113
 
1.2%
1010
 
0.9%
156
 
0.5%
136
 
0.5%
95
 
0.4%
124
 
0.4%
83
 
0.3%
192
 
0.2%
171
 
0.1%
161
 
0.1%
Other values (4)4
 
0.4%
(Missing)1057
95.1%
ValueCountFrequency (%)
41
 
0.1%
51
 
0.1%
83
 
0.3%
95
 
0.4%
1010
0.9%
1113
1.2%
124
 
0.4%
136
0.5%
141
 
0.1%
156
0.5%
ValueCountFrequency (%)
192
 
0.2%
181
 
0.1%
171
 
0.1%
161
 
0.1%
156
0.5%
141
 
0.1%
136
0.5%
124
 
0.4%
1113
1.2%
1010
0.9%

WISC_IV_PIC_CON_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)23.6%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean9.909090909
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:58.041871image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.7
Q17
median11
Q312
95-th percentile14
Maximum15
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.187200834
Coefficient of variation (CV)0.3216441209
Kurtosis-0.801485187
Mean9.909090909
Median Absolute Deviation (MAD)2
Skewness-0.4625943265
Sum545
Variance10.15824916
MonotonicityNot monotonic
2021-06-01T18:42:58.148468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1210
 
0.9%
138
 
0.7%
116
 
0.5%
76
 
0.5%
85
 
0.4%
104
 
0.4%
63
 
0.3%
53
 
0.3%
143
 
0.3%
152
 
0.2%
Other values (3)5
 
0.4%
(Missing)1057
95.1%
ValueCountFrequency (%)
32
 
0.2%
41
 
0.1%
53
 
0.3%
63
 
0.3%
76
0.5%
85
0.4%
92
 
0.2%
104
 
0.4%
116
0.5%
1210
0.9%
ValueCountFrequency (%)
152
 
0.2%
143
 
0.3%
138
0.7%
1210
0.9%
116
0.5%
104
 
0.4%
92
 
0.2%
85
0.4%
76
0.5%
63
 
0.3%

WISC_IV_MATRIX_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)20.0%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean11.54545455
Minimum7
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:58.242982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q110
median11
Q312.5
95-th percentile16.3
Maximum19
Range12
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.651471861
Coefficient of variation (CV)0.2296550431
Kurtosis0.4201510832
Mean11.54545455
Median Absolute Deviation (MAD)1
Skewness0.5998517863
Sum635
Variance7.03030303
MonotonicityNot monotonic
2021-06-01T18:42:58.345299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1112
 
1.1%
1010
 
0.9%
1210
 
0.9%
155
 
0.4%
134
 
0.4%
74
 
0.4%
93
 
0.3%
172
 
0.2%
162
 
0.2%
82
 
0.2%
(Missing)1057
95.1%
ValueCountFrequency (%)
74
 
0.4%
82
 
0.2%
93
 
0.3%
1010
0.9%
1112
1.1%
1210
0.9%
134
 
0.4%
155
0.4%
162
 
0.2%
172
 
0.2%
ValueCountFrequency (%)
191
 
0.1%
172
 
0.2%
162
 
0.2%
155
0.4%
134
 
0.4%
1210
0.9%
1112
1.1%
1010
0.9%
93
 
0.3%
82
 
0.2%

WISC_IV_DIGIT_SPAN_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)23.6%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean10.12727273
Minimum2
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:58.443558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q17.5
median10
Q312.5
95-th percentile15.6
Maximum17
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.232099108
Coefficient of variation (CV)0.3191480269
Kurtosis-0.06122914117
Mean10.12727273
Median Absolute Deviation (MAD)3
Skewness0.0486634223
Sum557
Variance10.44646465
MonotonicityNot monotonic
2021-06-01T18:42:58.542398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
119
 
0.8%
137
 
0.6%
77
 
0.6%
96
 
0.5%
106
 
0.5%
65
 
0.4%
173
 
0.3%
123
 
0.3%
83
 
0.3%
143
 
0.3%
Other values (3)3
 
0.3%
(Missing)1057
95.1%
ValueCountFrequency (%)
21
 
0.1%
41
 
0.1%
65
0.4%
77
0.6%
83
 
0.3%
96
0.5%
106
0.5%
119
0.8%
123
 
0.3%
137
0.6%
ValueCountFrequency (%)
173
 
0.3%
151
 
0.1%
143
 
0.3%
137
0.6%
123
 
0.3%
119
0.8%
106
0.5%
96
0.5%
83
 
0.3%
77
0.6%

WISC_IV_LET_NUM_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)23.6%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean10.36363636
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:58.644776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q19.5
median11
Q312
95-th percentile13
Maximum14
Range12
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.914609664
Coefficient of variation (CV)0.2812342658
Kurtosis0.9476406806
Mean10.36363636
Median Absolute Deviation (MAD)1
Skewness-1.352906483
Sum570
Variance8.494949495
MonotonicityNot monotonic
2021-06-01T18:42:58.875526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1217
 
1.5%
1113
 
1.2%
137
 
0.6%
64
 
0.4%
93
 
0.3%
102
 
0.2%
142
 
0.2%
42
 
0.2%
31
 
0.1%
81
 
0.1%
Other values (3)3
 
0.3%
(Missing)1057
95.1%
ValueCountFrequency (%)
21
 
0.1%
31
 
0.1%
42
 
0.2%
51
 
0.1%
64
 
0.4%
71
 
0.1%
81
 
0.1%
93
 
0.3%
102
 
0.2%
1113
1.2%
ValueCountFrequency (%)
142
 
0.2%
137
0.6%
1217
1.5%
1113
1.2%
102
 
0.2%
93
 
0.3%
81
 
0.1%
71
 
0.1%
64
 
0.4%
51
 
0.1%

WISC_IV_CODING_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)20.0%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean8.763636364
Minimum4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:58.978157image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q17
median9
Q310.5
95-th percentile13
Maximum15
Range11
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.821633598
Coefficient of variation (CV)0.3219706388
Kurtosis-0.5076136296
Mean8.763636364
Median Absolute Deviation (MAD)2
Skewness0.157900968
Sum482
Variance7.961616162
MonotonicityNot monotonic
2021-06-01T18:42:59.072605image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
99
 
0.8%
107
 
0.6%
87
 
0.6%
76
 
0.5%
55
 
0.4%
114
 
0.4%
124
 
0.4%
134
 
0.4%
44
 
0.4%
63
 
0.3%
(Missing)1057
95.1%
ValueCountFrequency (%)
44
0.4%
55
0.4%
63
 
0.3%
76
0.5%
87
0.6%
99
0.8%
107
0.6%
114
0.4%
124
0.4%
134
0.4%
ValueCountFrequency (%)
152
 
0.2%
134
0.4%
124
0.4%
114
0.4%
107
0.6%
99
0.8%
87
0.6%
76
0.5%
63
 
0.3%
55
0.4%

WISC_IV_SYM_SCALED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)21.8%
Missing1057
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean9.890909091
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:59.167996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median10
Q312
95-th percentile13.3
Maximum16
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.753265585
Coefficient of variation (CV)0.2783632485
Kurtosis1.302629264
Mean9.890909091
Median Absolute Deviation (MAD)2
Skewness-0.5284831181
Sum544
Variance7.58047138
MonotonicityNot monotonic
2021-06-01T18:42:59.267982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1012
 
1.1%
118
 
0.7%
128
 
0.7%
87
 
0.6%
95
 
0.4%
134
 
0.4%
63
 
0.3%
53
 
0.3%
162
 
0.2%
71
 
0.1%
Other values (2)2
 
0.2%
(Missing)1057
95.1%
ValueCountFrequency (%)
11
 
0.1%
53
 
0.3%
63
 
0.3%
71
 
0.1%
87
0.6%
95
0.4%
1012
1.1%
118
0.7%
128
0.7%
134
 
0.4%
ValueCountFrequency (%)
162
 
0.2%
141
 
0.1%
134
 
0.4%
128
0.7%
118
0.7%
1012
1.1%
95
0.4%
87
0.6%
71
 
0.1%
63
 
0.3%

EYE_STATUS_AT_SCAN
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.1 KiB
1
765 
2
347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,112
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1765
68.8%
2347
31.2%

Length

2021-06-01T18:42:59.499458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:42:59.586175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1765
68.8%
2347
31.2%

Most occurring characters

ValueCountFrequency (%)
1765
68.8%
2347
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1112
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1765
68.8%
2347
31.2%

Most occurring scripts

ValueCountFrequency (%)
Common1112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1765
68.8%
2347
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1765
68.8%
2347
31.2%

AGE_AT_MPRAGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct97
Distinct (%)97.0%
Missing1012
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean12.9517
Minimum8.29
Maximum17.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:59.693444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum8.29
5-th percentile9.6685
Q111.265
median12.945
Q314.4625
95-th percentile16.8965
Maximum17.94
Range9.65
Interquartile range (IQR)3.1975

Descriptive statistics

Standard deviation2.193554864
Coefficient of variation (CV)0.1693642428
Kurtosis-0.3754804416
Mean12.9517
Median Absolute Deviation (MAD)1.63
Skewness0.2478214906
Sum1295.17
Variance4.811682939
MonotonicityNot monotonic
2021-06-01T18:42:59.835672image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.562
 
0.2%
13.122
 
0.2%
14.032
 
0.2%
11.161
 
0.1%
12.331
 
0.1%
15.041
 
0.1%
13.461
 
0.1%
14.531
 
0.1%
10.011
 
0.1%
12.241
 
0.1%
Other values (87)87
 
7.8%
(Missing)1012
91.0%
ValueCountFrequency (%)
8.291
0.1%
8.491
0.1%
9.111
0.1%
9.211
0.1%
9.261
0.1%
9.691
0.1%
9.991
0.1%
10.011
0.1%
10.081
0.1%
10.331
0.1%
ValueCountFrequency (%)
17.941
0.1%
17.781
0.1%
17.711
0.1%
17.531
0.1%
17.41
0.1%
16.871
0.1%
16.631
0.1%
16.561
0.1%
16.031
0.1%
15.921
0.1%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct89
Distinct (%)94.7%
Missing1018
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean20.51925532
Minimum6.53
Maximum35.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:42:59.977525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum6.53
5-th percentile13.64
Q117.4225
median19.945
Q322.645
95-th percentile30.673
Maximum35.43
Range28.9
Interquartile range (IQR)5.2225

Descriptive statistics

Standard deviation5.244445754
Coefficient of variation (CV)0.2555865538
Kurtosis0.9127367968
Mean20.51925532
Median Absolute Deviation (MAD)2.645
Skewness0.7351197091
Sum1928.81
Variance27.50421127
MonotonicityNot monotonic
2021-06-01T18:43:00.122258image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.042
 
0.2%
18.182
 
0.2%
13.512
 
0.2%
20.272
 
0.2%
16.982
 
0.2%
24.121
 
0.1%
32.531
 
0.1%
6.531
 
0.1%
21.61
 
0.1%
13.711
 
0.1%
Other values (79)79
 
7.1%
(Missing)1018
91.5%
ValueCountFrequency (%)
6.531
0.1%
11.911
0.1%
13.421
0.1%
13.512
0.2%
13.711
0.1%
14.061
0.1%
14.631
0.1%
14.91
0.1%
151
0.1%
15.042
0.2%
ValueCountFrequency (%)
35.431
0.1%
34.771
0.1%
34.451
0.1%
32.531
0.1%
30.791
0.1%
30.611
0.1%
29.621
0.1%
28.691
0.1%
28.681
0.1%
27.941
0.1%

anat_cnr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1098
Distinct (%)99.9%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean11.64152656
Minimum1.5127 × 10-5
Maximum51.69180021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:00.418323image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.5127 × 10-5
5-th percentile4.805779058
Q18.613272519
median10.966648
Q313.18102109
95-th percentile21.92592207
Maximum51.69180021
Range51.69178508
Interquartile range (IQR)4.567748574

Descriptive statistics

Standard deviation5.612931869
Coefficient of variation (CV)0.4821474091
Kurtosis9.3192179
Mean11.64152656
Median Absolute Deviation (MAD)2.305556367
Skewness2.29460226
Sum12794.03769
Variance31.50500416
MonotonicityNot monotonic
2021-06-01T18:43:00.629264image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1248827842
 
0.2%
22.847494321
 
0.1%
8.382754191
 
0.1%
4.2273067231
 
0.1%
16.376528251
 
0.1%
16.552095481
 
0.1%
9.4634010621
 
0.1%
8.367971841
 
0.1%
16.02979371
 
0.1%
12.13594311
 
0.1%
Other values (1088)1088
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
1.5127 × 10-51
0.1%
2.633649449 × 10-51
0.1%
3.346339544 × 10-51
0.1%
4.475408081 × 10-51
0.1%
6.00567138 × 10-51
0.1%
6.053993374 × 10-51
0.1%
6.2718 × 10-51
0.1%
2.6875997191
0.1%
2.7577348591
0.1%
2.7903053321
0.1%
ValueCountFrequency (%)
51.691800211
0.1%
48.209544221
0.1%
42.753659151
0.1%
41.387089111
0.1%
40.811125331
0.1%
40.160774761
0.1%
39.7952671
0.1%
36.80636451
0.1%
36.345038991
0.1%
36.168330611
0.1%

anat_efc
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1098
Distinct (%)99.9%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2.079627907
Minimum-217.5604332
Maximum33.31837043
Zeros0
Zeros (%)0.0%
Negative36
Negative (%)3.2%
Memory size8.8 KiB
2021-06-01T18:43:00.819996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-217.5604332
5-th percentile0.4083417911
Q10.7557640794
median1.675090471
Q33.19733524
95-th percentile14.75046057
Maximum33.31837043
Range250.8788037
Interquartile range (IQR)2.441571161

Descriptive statistics

Standard deviation11.43527841
Coefficient of variation (CV)5.498713672
Kurtosis149.0742053
Mean2.079627907
Median Absolute Deviation (MAD)1.075775829
Skewness-9.733207898
Sum2285.51107
Variance130.7655922
MonotonicityNot monotonic
2021-06-01T18:43:00.989042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.39242908922
 
0.2%
0.4584284691
 
0.1%
1.4752936671
 
0.1%
0.43843402141
 
0.1%
4.0299978411
 
0.1%
-19.293769031
 
0.1%
0.50454464591
 
0.1%
4.0319380121
 
0.1%
1.6549362231
 
0.1%
1.3558945361
 
0.1%
Other values (1088)1088
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
-217.56043321
0.1%
-124.26749991
0.1%
-89.204127171
0.1%
-77.900490961
0.1%
-70.728725221
0.1%
-66.577096361
0.1%
-65.362714511
0.1%
-63.014316441
0.1%
-54.898813571
0.1%
-54.79138591
0.1%
ValueCountFrequency (%)
33.318370431
0.1%
25.090532261
0.1%
24.027729181
0.1%
22.758429261
0.1%
21.031152841
0.1%
20.907226361
0.1%
20.368314561
0.1%
20.06328851
0.1%
19.551894171
0.1%
19.0413791
0.1%

anat_fber
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1098
Distinct (%)99.9%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean72.80497076
Minimum-3.123742768
Maximum1734.146859
Zeros0
Zeros (%)0.0%
Negative56
Negative (%)5.0%
Memory size8.8 KiB
2021-06-01T18:43:01.163556image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-3.123742768
5-th percentile-0.00371319248
Q13.898698599
median10.34174205
Q386.02800772
95-th percentile340.7688529
Maximum1734.146859
Range1737.270602
Interquartile range (IQR)82.12930912

Descriptive statistics

Standard deviation155.0336405
Coefficient of variation (CV)2.129437577
Kurtosis27.6370819
Mean72.80497076
Median Absolute Deviation (MAD)9.505317097
Skewness4.485686883
Sum80012.66287
Variance24035.42969
MonotonicityNot monotonic
2021-06-01T18:43:01.350404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
607.82302342
 
0.2%
0.95225419431
 
0.1%
141.85418311
 
0.1%
0.15994535421
 
0.1%
1.9940991971
 
0.1%
19.531834311
 
0.1%
819.50815121
 
0.1%
64.08942451
 
0.1%
6.3186258951
 
0.1%
86.854893421
 
0.1%
Other values (1088)1088
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
-3.1237427681
0.1%
-3.0141564691
0.1%
-1.71387151
0.1%
-1.3492273361
0.1%
-1.3405574611
0.1%
-1.2916476851
0.1%
-1.1717599031
0.1%
-1.1307643441
0.1%
-1.0092779741
0.1%
-0.97911631741
0.1%
ValueCountFrequency (%)
1734.1468591
0.1%
1394.3410751
0.1%
1093.8248291
0.1%
1089.1763431
0.1%
1040.224571
0.1%
977.44123011
0.1%
937.16881221
0.1%
915.48522041
0.1%
893.10119661
0.1%
877.00784611
0.1%

anat_fwhm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1098
Distinct (%)99.9%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean3.558792824
Minimum2.533930151
Maximum5.938324474
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:01.535046image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2.533930151
5-th percentile2.715019176
Q13.088845903
median3.401204822
Q33.831770791
95-th percentile4.952873978
Maximum5.938324474
Range3.404394323
Interquartile range (IQR)0.7429248876

Descriptive statistics

Standard deviation0.6763119467
Coefficient of variation (CV)0.1900397073
Kurtosis0.4410845834
Mean3.558792824
Median Absolute Deviation (MAD)0.3583151781
Skewness1.013056189
Sum3911.113314
Variance0.4573978493
MonotonicityNot monotonic
2021-06-01T18:43:01.877870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2551026732
 
0.2%
3.4852258261
 
0.1%
2.8398209421
 
0.1%
3.243031
 
0.1%
4.4023902621
 
0.1%
3.7251858151
 
0.1%
4.9083034511
 
0.1%
3.1383572941
 
0.1%
2.7231512931
 
0.1%
4.4258691831
 
0.1%
Other values (1088)1088
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
2.5339301511
0.1%
2.6054307521
0.1%
2.6088842871
0.1%
2.6150343431
0.1%
2.6156064411
0.1%
2.6168815771
0.1%
2.617501211
0.1%
2.6238685411
0.1%
2.6264607551
0.1%
2.6320118381
0.1%
ValueCountFrequency (%)
5.9383244741
0.1%
5.928331
0.1%
5.7383193771
0.1%
5.6305565281
0.1%
5.6181280991
0.1%
5.5601685971
0.1%
5.5575992581
0.1%
5.556224961
0.1%
5.5308004571
0.1%
5.5016265121
0.1%

anat_qi1
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1098
Distinct (%)99.9%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.07220874586
Minimum0.000451496
Maximum0.2590483255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:02.028535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.000451496
5-th percentile0.0036606195
Q10.04001489865
median0.0609164795
Q30.09353427935
95-th percentile0.1816229217
Maximum0.2590483255
Range0.2585968295
Interquartile range (IQR)0.0535193807

Descriptive statistics

Standard deviation0.05228311564
Coefficient of variation (CV)0.7240551684
Kurtosis0.7935996427
Mean0.07220874586
Median Absolute Deviation (MAD)0.0250083936
Skewness1.032139006
Sum79.3574117
Variance0.002733524181
MonotonicityNot monotonic
2021-06-01T18:43:02.181281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00636619842
 
0.2%
0.00298177331
 
0.1%
0.0248018661
 
0.1%
0.0728091321
 
0.1%
0.04468939011
 
0.1%
0.01294521421
 
0.1%
0.18048279551
 
0.1%
0.04844181831
 
0.1%
0.14176073581
 
0.1%
0.05057527731
 
0.1%
Other values (1088)1088
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0.0004514961
0.1%
0.00066975021
0.1%
0.00067993031
0.1%
0.00070784571
0.1%
0.00073333091
0.1%
0.00076333951
0.1%
0.00102514281
0.1%
0.00107043151
0.1%
0.0012039011
0.1%
0.0012473291
0.1%
ValueCountFrequency (%)
0.25904832551
0.1%
0.2559177391
0.1%
0.24975719811
0.1%
0.24508616761
0.1%
0.24197673391
0.1%
0.23864909091
0.1%
0.2362143061
0.1%
0.23236214471
0.1%
0.23215741941
0.1%
0.22775846261
0.1%

anat_snr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1098
Distinct (%)99.9%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean48.18579258
Minimum0.0014000718
Maximum5957.198529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:02.335654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0014000718
5-th percentile7.810063167
Q112.26275613
median15.6018415
Q321.52838598
95-th percentile49.87890098
Maximum5957.198529
Range5957.197129
Interquartile range (IQR)9.265629848

Descriptive statistics

Standard deviation282.7366529
Coefficient of variation (CV)5.867635204
Kurtosis222.3116355
Mean48.18579258
Median Absolute Deviation (MAD)4.267211013
Skewness13.55347301
Sum52956.18605
Variance79940.01489
MonotonicityNot monotonic
2021-06-01T18:43:02.483250image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.704241582
 
0.2%
9.3234627731
 
0.1%
30.619479651
 
0.1%
14.942991041
 
0.1%
24.353702651
 
0.1%
12.164886421
 
0.1%
14.39000851
 
0.1%
15.478345251
 
0.1%
19.083947681
 
0.1%
21.132861411
 
0.1%
Other values (1088)1088
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0.00140007181
0.1%
0.00421907291
0.1%
0.00728094661
0.1%
0.02111404351
0.1%
0.02319620091
0.1%
0.02528815581
0.1%
0.02647584691
0.1%
4.0746306671
0.1%
4.5162504111
0.1%
4.5734678891
0.1%
ValueCountFrequency (%)
5957.1985291
0.1%
3115.92171
0.1%
3065.0351211
0.1%
3012.7424121
0.1%
2800.4699541
0.1%
2373.0204561
0.1%
1700.17731
0.1%
1413.9292971
0.1%
1393.9889971
0.1%
1163.7055691
0.1%

func_efc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1097
Distinct (%)99.8%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.5087068598
Minimum0.3523913071
Maximum0.6682792416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:02.632077image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.3523913071
5-th percentile0.3924322597
Q10.4712256888
median0.517312364
Q30.550616871
95-th percentile0.5942621775
Maximum0.6682792416
Range0.3158879345
Interquartile range (IQR)0.07939118225

Descriptive statistics

Standard deviation0.05983152613
Coefficient of variation (CV)0.1176149387
Kurtosis-0.2417579795
Mean0.5087068598
Median Absolute Deviation (MAD)0.0385879217
Skewness-0.4327703683
Sum559.0688389
Variance0.003579811519
MonotonicityNot monotonic
2021-06-01T18:43:02.779976image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54209288172
 
0.2%
0.48701402712
 
0.2%
0.55666930261
 
0.1%
0.47798210521
 
0.1%
0.38540022351
 
0.1%
0.55135079551
 
0.1%
0.55751233421
 
0.1%
0.44318268111
 
0.1%
0.51117741961
 
0.1%
0.54188828771
 
0.1%
Other values (1087)1087
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0.35239130711
0.1%
0.35343721581
0.1%
0.35446040221
0.1%
0.35512469171
0.1%
0.35625528711
0.1%
0.35807372311
0.1%
0.35897220881
0.1%
0.35901968261
0.1%
0.36002886411
0.1%
0.36043154781
0.1%
ValueCountFrequency (%)
0.66827924161
0.1%
0.66527882921
0.1%
0.65850215751
0.1%
0.65275181111
0.1%
0.64798224421
0.1%
0.63816675071
0.1%
0.63393257651
0.1%
0.63076829981
0.1%
0.62755132211
0.1%
0.62480110711
0.1%

func_fber
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1097
Distinct (%)99.8%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean100.5484982
Minimum33.83651294
Maximum326.544102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:02.944638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum33.83651294
5-th percentile51.40009785
Q169.04720945
median87.68133368
Q3125.7792688
95-th percentile186.319532
Maximum326.544102
Range292.7075891
Interquartile range (IQR)56.73205931

Descriptive statistics

Standard deviation43.55297005
Coefficient of variation (CV)0.4331538592
Kurtosis2.027807685
Mean100.5484982
Median Absolute Deviation (MAD)24.74184499
Skewness1.290097044
Sum110502.7996
Variance1896.8612
MonotonicityNot monotonic
2021-06-01T18:43:03.154244image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.131188342
 
0.2%
125.77926882
 
0.2%
191.68748111
 
0.1%
132.57323191
 
0.1%
77.981574561
 
0.1%
57.37277311
 
0.1%
57.074738581
 
0.1%
169.95965481
 
0.1%
86.133581091
 
0.1%
137.54731821
 
0.1%
Other values (1087)1087
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
33.836512941
0.1%
39.602081261
0.1%
40.857428851
0.1%
41.857514931
0.1%
42.197279671
0.1%
42.354811591
0.1%
42.466819371
0.1%
42.819006461
0.1%
43.369661251
0.1%
44.068321681
0.1%
ValueCountFrequency (%)
326.5441021
0.1%
300.16928081
0.1%
289.65651551
0.1%
285.23569891
0.1%
264.50467371
0.1%
261.29424691
0.1%
260.20928151
0.1%
259.28547151
0.1%
258.90640561
0.1%
240.87496761
0.1%

func_fwhm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1096
Distinct (%)99.7%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2.116388137
Minimum1.581107797
Maximum3.753480876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:03.308607image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.581107797
5-th percentile1.740359221
Q11.862772151
median2.004123454
Q32.328370479
95-th percentile2.784598104
Maximum3.753480876
Range2.172373079
Interquartile range (IQR)0.4655983279

Descriptive statistics

Standard deviation0.3452822569
Coefficient of variation (CV)0.163146944
Kurtosis1.171348141
Mean2.116388137
Median Absolute Deviation (MAD)0.1861831526
Skewness1.167681459
Sum2325.910562
Variance0.1192198369
MonotonicityNot monotonic
2021-06-01T18:43:03.451263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3407188182
 
0.2%
2.5242273282
 
0.2%
1.8869465712
 
0.2%
1.8563765441
 
0.1%
2.3996033281
 
0.1%
1.7639637161
 
0.1%
1.9515756291
 
0.1%
1.8137105511
 
0.1%
2.3502456611
 
0.1%
1.8646806661
 
0.1%
Other values (1086)1086
97.7%
(Missing)13
 
1.2%
ValueCountFrequency (%)
1.5811077971
0.1%
1.595247241
0.1%
1.6147232841
0.1%
1.6210886611
0.1%
1.6228189951
0.1%
1.6287150191
0.1%
1.6318948511
0.1%
1.6347603091
0.1%
1.6405401061
0.1%
1.6475319411
0.1%
ValueCountFrequency (%)
3.7534808761
0.1%
3.5406897081
0.1%
3.4893605541
0.1%
3.448406721
0.1%
3.3332416771
0.1%
3.2906185461
0.1%
3.2682189091
0.1%
3.2346116441
0.1%
3.2176780871
0.1%
3.2151963021
0.1%

func_dvars
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1097
Distinct (%)99.8%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1.103530605
Minimum0.7096706494
Maximum1.571100561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:03.608946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.7096706494
5-th percentile0.9451891179
Q11.026977328
median1.080726744
Q31.172068384
95-th percentile1.323812559
Maximum1.571100561
Range0.8614299119
Interquartile range (IQR)0.1450910565

Descriptive statistics

Standard deviation0.1178028823
Coefficient of variation (CV)0.1067508974
Kurtosis0.7788155316
Mean1.103530605
Median Absolute Deviation (MAD)0.0682732949
Skewness0.6738071684
Sum1212.780135
Variance0.01387751908
MonotonicityNot monotonic
2021-06-01T18:43:03.770667image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.91039935992
 
0.2%
1.0964379822
 
0.2%
1.0242245381
 
0.1%
1.148767981
 
0.1%
1.1166749231
 
0.1%
1.0199210491
 
0.1%
0.9692220951
 
0.1%
1.1684834151
 
0.1%
1.1746012171
 
0.1%
0.96092370441
 
0.1%
Other values (1087)1087
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0.70967064941
0.1%
0.78473219761
0.1%
0.79071586921
0.1%
0.81947445131
0.1%
0.8336864371
0.1%
0.83512244931
0.1%
0.84888263591
0.1%
0.85164411231
0.1%
0.85224990791
0.1%
0.85617006961
0.1%
ValueCountFrequency (%)
1.5711005611
0.1%
1.5489681251
0.1%
1.5083219061
0.1%
1.5047061451
0.1%
1.4994826581
0.1%
1.4660218971
0.1%
1.4599866851
0.1%
1.459718321
0.1%
1.4597095151
0.1%
1.4532178011
0.1%

func_outlier
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1097
Distinct (%)99.8%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.005525124257
Minimum0.0001820833
Maximum0.0801582333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:03.935272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0001820833
5-th percentile0.00043095333
Q10.0011242631
median0.00344115
Q30.0069767642
95-th percentile0.01836710536
Maximum0.0801582333
Range0.07997615
Interquartile range (IQR)0.0058525011

Descriptive statistics

Standard deviation0.006463663634
Coefficient of variation (CV)1.16986756
Kurtosis20.25400842
Mean0.005525124257
Median Absolute Deviation (MAD)0.00272465
Skewness3.180979782
Sum6.072111558
Variance4.177894757 × 10-5
MonotonicityNot monotonic
2021-06-01T18:43:04.086097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02740666672
 
0.2%
0.017047852
 
0.2%
0.00577505561
 
0.1%
0.00896519051
 
0.1%
0.00645466671
 
0.1%
0.00555633331
 
0.1%
0.027625251
 
0.1%
0.02160033331
 
0.1%
0.00618433331
 
0.1%
0.00605044441
 
0.1%
Other values (1087)1087
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0.00018208331
0.1%
0.00018766671
0.1%
0.00019706671
0.1%
0.00020358331
0.1%
0.0002141
0.1%
0.00021829171
0.1%
0.0002188751
0.1%
0.0002361
0.1%
0.00024046671
0.1%
0.00024841
0.1%
ValueCountFrequency (%)
0.08015823331
0.1%
0.04430583331
0.1%
0.03669781
0.1%
0.03561677781
0.1%
0.033968561
0.1%
0.0336541
0.1%
0.03227646671
0.1%
0.031603451
0.1%
0.031595651
0.1%
0.029918851
0.1%

func_quality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1097
Distinct (%)99.8%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.01277813038
Minimum0.0016933026
Maximum0.0986142022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:04.244196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0016933026
5-th percentile0.00301223625
Q10.0063329529
median0.0100427358
Q30.0151894801
95-th percentile0.03025089608
Maximum0.0986142022
Range0.0969208996
Interquartile range (IQR)0.0088565272

Descriptive statistics

Standard deviation0.01121154649
Coefficient of variation (CV)0.8774011659
Kurtosis18.69952816
Mean0.01277813038
Median Absolute Deviation (MAD)0.004217216
Skewness3.55701576
Sum14.04316528
Variance0.0001256987747
MonotonicityNot monotonic
2021-06-01T18:43:04.588376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01548032152
 
0.2%
0.0965606092
 
0.2%
0.01102569151
 
0.1%
0.01477078991
 
0.1%
0.0112769921
 
0.1%
0.01735443951
 
0.1%
0.00896568231
 
0.1%
0.00227086681
 
0.1%
0.02217797071
 
0.1%
0.00814794711
 
0.1%
Other values (1087)1087
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0.00169330261
0.1%
0.00173710011
0.1%
0.00174700561
0.1%
0.00183019751
0.1%
0.00200039881
0.1%
0.00201718641
0.1%
0.00206395921
0.1%
0.00207309581
0.1%
0.00211550621
0.1%
0.00216281031
0.1%
ValueCountFrequency (%)
0.09861420221
0.1%
0.0965606092
0.2%
0.09466201271
0.1%
0.08383219781
0.1%
0.08339294821
0.1%
0.0826782641
0.1%
0.07531792481
0.1%
0.07262824331
0.1%
0.06955753331
0.1%
0.06793066171
0.1%

func_mean_fd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1097
Distinct (%)99.8%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.1311382432
Minimum0.0161171065
Maximum1.434911749
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:04.733683image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0161171065
5-th percentile0.03012053155
Q10.05088801175
median0.0831633456
Q30.1434788863
95-th percentile0.3913830207
Maximum1.434911749
Range1.418794642
Interquartile range (IQR)0.09259087455

Descriptive statistics

Standard deviation0.1577280329
Coefficient of variation (CV)1.202761521
Kurtosis20.80234657
Mean0.1311382432
Median Absolute Deviation (MAD)0.0382794494
Skewness4.007492775
Sum144.1209293
Variance0.02487813236
MonotonicityNot monotonic
2021-06-01T18:43:04.876513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1397483622
 
0.2%
0.20734317762
 
0.2%
0.03249581861
 
0.1%
0.06689049691
 
0.1%
0.07283975621
 
0.1%
0.12855291561
 
0.1%
0.09587837211
 
0.1%
0.08902752831
 
0.1%
0.09772477981
 
0.1%
0.07496577691
 
0.1%
Other values (1087)1087
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0.01611710651
0.1%
0.01799372521
0.1%
0.01974201111
0.1%
0.02081984721
0.1%
0.02125462291
0.1%
0.0214628841
0.1%
0.02150348651
0.1%
0.02207630791
0.1%
0.02230125311
0.1%
0.02237271681
0.1%
ValueCountFrequency (%)
1.4349117491
0.1%
1.3246968251
0.1%
1.2243426881
0.1%
1.1989058561
0.1%
1.1397483622
0.2%
1.0945172561
0.1%
1.0562528381
0.1%
1.052228531
0.1%
1.0305380111
0.1%
1.0121977171
0.1%

func_num_fd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct153
Distinct (%)13.9%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean27.21838035
Minimum0
Maximum288
Zeros157
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:05.034896image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q333
95-th percentile123.4
Maximum288
Range288
Interquartile range (IQR)31

Descriptive statistics

Standard deviation42.75758317
Coefficient of variation (CV)1.570908431
Kurtosis7.554736581
Mean27.21838035
Median Absolute Deviation (MAD)9
Skewness2.581581477
Sum29913
Variance1828.210919
MonotonicityNot monotonic
2021-06-01T18:43:05.178632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0157
 
14.1%
1106
 
9.5%
261
 
5.5%
352
 
4.7%
839
 
3.5%
436
 
3.2%
733
 
3.0%
526
 
2.3%
1025
 
2.2%
624
 
2.2%
Other values (143)540
48.6%
ValueCountFrequency (%)
0157
14.1%
1106
9.5%
261
 
5.5%
352
 
4.7%
436
 
3.2%
526
 
2.3%
624
 
2.2%
733
 
3.0%
839
 
3.5%
919
 
1.7%
ValueCountFrequency (%)
2881
 
0.1%
2741
 
0.1%
2541
 
0.1%
2511
 
0.1%
2401
 
0.1%
2233
0.3%
2151
 
0.1%
2131
 
0.1%
1971
 
0.1%
1961
 
0.1%

func_perc_fd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct451
Distinct (%)41.0%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.95282587
Minimum0
Maximum91.73553719
Zeros157
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2021-06-01T18:43:05.513352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.9950248756
median4.972375691
Q316.97798486
95-th percentile56.25015419
Maximum91.73553719
Range91.73553719
Interquartile range (IQR)15.98295999

Descriptive statistics

Standard deviation18.21667123
Coefficient of variation (CV)1.406385866
Kurtosis3.606453968
Mean12.95282587
Median Absolute Deviation (MAD)4.972375691
Skewness1.983002178
Sum14235.15563
Variance331.8471105
MonotonicityNot monotonic
2021-06-01T18:43:05.796117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0157
 
14.1%
0.552486187848
 
4.3%
1.10497237620
 
1.8%
1.65745856414
 
1.3%
0.82644628114
 
1.3%
2.20994475113
 
1.2%
0.662251655612
 
1.1%
0.99502487569
 
0.8%
0.99667774099
 
0.8%
6.6115702489
 
0.8%
Other values (441)794
71.4%
(Missing)13
 
1.2%
ValueCountFrequency (%)
0157
14.1%
0.33222591368
 
0.7%
0.39840637454
 
0.4%
0.41493775937
 
0.6%
0.47393364932
 
0.2%
0.49751243782
 
0.2%
0.552486187848
 
4.3%
0.63694267521
 
0.1%
0.662251655612
 
1.1%
0.66445182727
 
0.6%
ValueCountFrequency (%)
91.735537191
0.1%
91.029900331
0.1%
89.719626171
0.1%
89.054726371
0.1%
88.429752071
0.1%
84.385382061
0.1%
83.886255921
0.1%
83.388704321
0.1%
80.497925311
0.1%
77.685950412
0.2%

func_gsr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1097
Distinct (%)99.8%
Missing13
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.02841552469
Minimum-0.0158118587
Maximum0.1042251438
Zeros0
Zeros (%)0.0%
Negative162
Negative (%)14.6%
Memory size8.8 KiB
2021-06-01T18:43:06.017457image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0158118587
5-th percentile-0.00703641833
Q10.01307549095
median0.031101834
Q30.04282148745
95-th percentile0.0611214455
Maximum0.1042251438
Range0.1200370025
Interquartile range (IQR)0.0297459965

Descriptive statistics

Standard deviation0.02106960751
Coefficient of variation (CV)0.7414822615
Kurtosis-0.5223787308
Mean0.02841552469
Median Absolute Deviation (MAD)0.014553478
Skewness-0.08993387301
Sum31.22866163
Variance0.0004439283606
MonotonicityNot monotonic
2021-06-01T18:43:06.202680image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03730666262
 
0.2%
0.04001497852
 
0.2%
-0.00491398531
 
0.1%
0.01284581981
 
0.1%
0.03976666551
 
0.1%
0.03448424861
 
0.1%
0.05032333361
 
0.1%
0.01777571071
 
0.1%
0.02879172381
 
0.1%
0.05331080031
 
0.1%
Other values (1087)1087
97.8%
(Missing)13
 
1.2%
ValueCountFrequency (%)
-0.01581185871
0.1%
-0.01561757361
0.1%
-0.01414134261
0.1%
-0.01343384291
0.1%
-0.01316136121
0.1%
-0.01238835511
0.1%
-0.0121879011
0.1%
-0.01158561741
0.1%
-0.0115266881
0.1%
-0.01133575661
0.1%
ValueCountFrequency (%)
0.10422514381
0.1%
0.08258271961
0.1%
0.08047758791
0.1%
0.07929652931
0.1%
0.07895459981
0.1%
0.07829249651
0.1%
0.07634580141
0.1%
0.07594828311
0.1%
0.07552421091
0.1%
0.07545833291
0.1%

qc_rater_1
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size64.4 KiB
OK
997 
fail
111 
maybe
 
4

Length

Max length5
Median length2
Mean length2.210431655
Min length2

Characters and Unicode

Total characters2,458
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfail
2nd rowOK
3rd rowOK
4th rowOK
5th rowOK

Common Values

ValueCountFrequency (%)
OK997
89.7%
fail111
 
10.0%
maybe4
 
0.4%

Length

2021-06-01T18:43:06.515773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:43:06.611686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ok997
89.7%
fail111
 
10.0%
maybe4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
O997
40.6%
K997
40.6%
a115
 
4.7%
f111
 
4.5%
i111
 
4.5%
l111
 
4.5%
m4
 
0.2%
y4
 
0.2%
b4
 
0.2%
e4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1994
81.1%
Lowercase Letter464
 
18.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a115
24.8%
f111
23.9%
i111
23.9%
l111
23.9%
m4
 
0.9%
y4
 
0.9%
b4
 
0.9%
e4
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
O997
50.0%
K997
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2458
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O997
40.6%
K997
40.6%
a115
 
4.7%
f111
 
4.5%
i111
 
4.5%
l111
 
4.5%
m4
 
0.2%
y4
 
0.2%
b4
 
0.2%
e4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O997
40.6%
K997
40.6%
a115
 
4.7%
f111
 
4.5%
i111
 
4.5%
l111
 
4.5%
m4
 
0.2%
y4
 
0.2%
b4
 
0.2%
e4
 
0.2%

qc_notes_rater_1
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)46.2%
Missing1073
Missing (%)96.5%
Memory size36.6 KiB
dorsal cropped
16 
**Not recommended with niak
ventral edge is cropped
registration error (dparsf)
 
1
frontal lob gone, rest of brain warped
 
1
Other values (13)
13 

Length

Max length38
Median length22
Mean length20.76923077
Min length10

Characters and Unicode

Total characters810
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)38.5%

Sample

1st rowdorsal cropped
2nd rowcerebellum & brainstem cropped
3rd rowmisshapen right frontal lobe
4th rowleft frontal low signal
5th rowfrontal lobe hole. Sinus?

Common Values

ValueCountFrequency (%)
dorsal cropped16
 
1.4%
**Not recommended with niak4
 
0.4%
ventral edge is cropped4
 
0.4%
registration error (dparsf)1
 
0.1%
frontal lob gone, rest of brain warped1
 
0.1%
tip of frontal lobe cropped1
 
0.1%
cerebellum & brainstem cropped1
 
0.1%
front left cropped. Registration error1
 
0.1%
left frontal low signal1
 
0.1%
skullstrip error?1
 
0.1%
Other values (8)8
 
0.7%
(Missing)1073
96.5%

Length

2021-06-01T18:43:06.877855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cropped24
19.8%
dorsal17
 
14.0%
frontal6
 
5.0%
lobe5
 
4.1%
not5
 
4.1%
error4
 
3.3%
with4
 
3.3%
ventral4
 
3.3%
edge4
 
3.3%
recommended4
 
3.3%
Other values (29)44
36.4%

Most occurring characters

ValueCountFrequency (%)
r88
10.9%
o84
10.4%
82
10.1%
e74
 
9.1%
d59
 
7.3%
p54
 
6.7%
l50
 
6.2%
a45
 
5.6%
t40
 
4.9%
s37
 
4.6%
Other values (24)197
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter702
86.7%
Space Separator82
 
10.1%
Other Punctuation16
 
2.0%
Uppercase Letter8
 
1.0%
Open Punctuation1
 
0.1%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r88
12.5%
o84
12.0%
e74
10.5%
d59
8.4%
p54
 
7.7%
l50
 
7.1%
a45
 
6.4%
t40
 
5.7%
s37
 
5.3%
c32
 
4.6%
Other values (13)139
19.8%
Other Punctuation
ValueCountFrequency (%)
*8
50.0%
.4
25.0%
?2
 
12.5%
&1
 
6.2%
,1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
N5
62.5%
R2
 
25.0%
S1
 
12.5%
Space Separator
ValueCountFrequency (%)
82
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin710
87.7%
Common100
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r88
12.4%
o84
11.8%
e74
10.4%
d59
8.3%
p54
 
7.6%
l50
 
7.0%
a45
 
6.3%
t40
 
5.6%
s37
 
5.2%
c32
 
4.5%
Other values (16)147
20.7%
Common
ValueCountFrequency (%)
82
82.0%
*8
 
8.0%
.4
 
4.0%
?2
 
2.0%
&1
 
1.0%
,1
 
1.0%
(1
 
1.0%
)1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r88
10.9%
o84
10.4%
82
10.1%
e74
 
9.1%
d59
 
7.3%
p54
 
6.7%
l50
 
6.2%
a45
 
5.6%
t40
 
4.9%
s37
 
4.6%
Other values (24)197
24.3%

qc_anat_rater_2
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.3%
Missing12
Missing (%)1.1%
Memory size64.5 KiB
OK
866 
maybe
194 
fail
 
40

Length

Max length5
Median length2
Mean length2.601818182
Min length2

Characters and Unicode

Total characters2,862
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOK
2nd rowOK
3rd rowOK
4th rowOK
5th rowOK

Common Values

ValueCountFrequency (%)
OK866
77.9%
maybe194
 
17.4%
fail40
 
3.6%
(Missing)12
 
1.1%

Length

2021-06-01T18:43:07.178182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:43:07.270325image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ok866
78.7%
maybe194
 
17.6%
fail40
 
3.6%

Most occurring characters

ValueCountFrequency (%)
O866
30.3%
K866
30.3%
a234
 
8.2%
m194
 
6.8%
y194
 
6.8%
b194
 
6.8%
e194
 
6.8%
f40
 
1.4%
i40
 
1.4%
l40
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1732
60.5%
Lowercase Letter1130
39.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a234
20.7%
m194
17.2%
y194
17.2%
b194
17.2%
e194
17.2%
f40
 
3.5%
i40
 
3.5%
l40
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
O866
50.0%
K866
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2862
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O866
30.3%
K866
30.3%
a234
 
8.2%
m194
 
6.8%
y194
 
6.8%
b194
 
6.8%
e194
 
6.8%
f40
 
1.4%
i40
 
1.4%
l40
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O866
30.3%
K866
30.3%
a234
 
8.2%
m194
 
6.8%
y194
 
6.8%
b194
 
6.8%
e194
 
6.8%
f40
 
1.4%
i40
 
1.4%
l40
 
1.4%

qc_anat_notes_rater_2
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)5.8%
Missing837
Missing (%)75.3%
Memory size46.3 KiB
skull-striping fail
98 
skull-striping fail;
59 
skull-striping fail; Motion
30 
stripe
29 
Motion
22 
Other values (11)
37 

Length

Max length37
Median length19
Mean length17.45818182
Min length6

Characters and Unicode

Total characters4,801
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.8%

Sample

1st rowskull-striping fail
2nd rowskull-striping fail
3rd rowskull-striping fail
4th rowskull-striping fail
5th rowskull-striping fail

Common Values

ValueCountFrequency (%)
skull-striping fail98
 
8.8%
skull-striping fail;59
 
5.3%
skull-striping fail; Motion30
 
2.7%
stripe29
 
2.6%
Motion22
 
2.0%
motion15
 
1.3%
skull-striping fail; motion artefacts5
 
0.4%
large-ventricals5
 
0.4%
motion, large ventricles3
 
0.3%
motion artefacts2
 
0.2%
Other values (6)7
 
0.6%
(Missing)837
75.3%

Length

2021-06-01T18:43:07.484502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fail197
37.2%
skull-striping197
37.2%
motion81
15.3%
stripe29
 
5.5%
artefacts7
 
1.3%
large-ventricals5
 
0.9%
large4
 
0.8%
ventricles4
 
0.8%
bad2
 
0.4%
cerebellum1
 
0.2%
Other values (3)3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i711
14.8%
l614
12.8%
s439
 
9.1%
t331
 
6.9%
n288
 
6.0%
255
 
5.3%
r253
 
5.3%
a227
 
4.7%
p226
 
4.7%
g206
 
4.3%
Other values (14)1251
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4188
87.2%
Space Separator255
 
5.3%
Dash Punctuation203
 
4.2%
Other Punctuation102
 
2.1%
Uppercase Letter53
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i711
17.0%
l614
14.7%
s439
10.5%
t331
7.9%
n288
6.9%
r253
 
6.0%
a227
 
5.4%
p226
 
5.4%
g206
 
4.9%
f205
 
4.9%
Other values (9)688
16.4%
Other Punctuation
ValueCountFrequency (%)
;99
97.1%
,3
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
-203
100.0%
Space Separator
ValueCountFrequency (%)
255
100.0%
Uppercase Letter
ValueCountFrequency (%)
M53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4241
88.3%
Common560
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i711
16.8%
l614
14.5%
s439
10.4%
t331
7.8%
n288
 
6.8%
r253
 
6.0%
a227
 
5.4%
p226
 
5.3%
g206
 
4.9%
f205
 
4.8%
Other values (10)741
17.5%
Common
ValueCountFrequency (%)
255
45.5%
-203
36.2%
;99
 
17.7%
,3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i711
14.8%
l614
12.8%
s439
 
9.1%
t331
 
6.9%
n288
 
6.0%
255
 
5.3%
r253
 
5.3%
a227
 
4.7%
p226
 
4.7%
g206
 
4.3%
Other values (14)1251
26.1%

qc_func_rater_2
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.3%
Missing12
Missing (%)1.1%
Memory size64.6 KiB
OK
842 
maybe
207 
fail
 
51

Length

Max length5
Median length2
Mean length2.657272727
Min length2

Characters and Unicode

Total characters2,923
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfail
2nd rowOK
3rd rowOK
4th rowmaybe
5th rowmaybe

Common Values

ValueCountFrequency (%)
OK842
75.7%
maybe207
 
18.6%
fail51
 
4.6%
(Missing)12
 
1.1%

Length

2021-06-01T18:43:07.879724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:43:07.961611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ok842
76.5%
maybe207
 
18.8%
fail51
 
4.6%

Most occurring characters

ValueCountFrequency (%)
O842
28.8%
K842
28.8%
a258
 
8.8%
m207
 
7.1%
y207
 
7.1%
b207
 
7.1%
e207
 
7.1%
f51
 
1.7%
i51
 
1.7%
l51
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1684
57.6%
Lowercase Letter1239
42.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a258
20.8%
m207
16.7%
y207
16.7%
b207
16.7%
e207
16.7%
f51
 
4.1%
i51
 
4.1%
l51
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
O842
50.0%
K842
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2923
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O842
28.8%
K842
28.8%
a258
 
8.8%
m207
 
7.1%
y207
 
7.1%
b207
 
7.1%
e207
 
7.1%
f51
 
1.7%
i51
 
1.7%
l51
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O842
28.8%
K842
28.8%
a258
 
8.8%
m207
 
7.1%
y207
 
7.1%
b207
 
7.1%
e207
 
7.1%
f51
 
1.7%
i51
 
1.7%
l51
 
1.7%

qc_func_notes_rater_2
Categorical

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)9.7%
Missing853
Missing (%)76.7%
Memory size45.6 KiB
ic-cerebellum
130 
Ic-parietal
27 
ic-cerebellum-temporal_lobe
16 
ic-cerebellum_temporal_lobe
16 
ic-parietal-cerebellum
 
13
Other values (20)
57 

Length

Max length39
Median length13
Mean length17.39382239
Min length2

Characters and Unicode

Total characters4,505
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)3.5%

Sample

1st rowic-parietal-cerebellum
2nd rowic-parietal-cerebellum
3rd rowic-parietal slight
4th rowic-cerebellum_temporal_lob
5th rowic-parietal-cerebellum

Common Values

ValueCountFrequency (%)
ic-cerebellum130
 
11.7%
Ic-parietal27
 
2.4%
ic-cerebellum-temporal_lobe16
 
1.4%
ic-cerebellum_temporal_lobe16
 
1.4%
ic-parietal-cerebellum13
 
1.2%
ic-cerebellum_temporal_lob9
 
0.8%
ic-frontal-temporal-cerebellum8
 
0.7%
Ic-parietal-minor6
 
0.5%
ic-temporal-cerebellum6
 
0.5%
ic-parietal slight4
 
0.4%
Other values (15)24
 
2.2%
(Missing)853
76.7%

Length

2021-06-01T18:43:08.194117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ic-cerebellum132
47.3%
ic-parietal33
 
11.8%
ic-cerebellum_temporal_lobe16
 
5.7%
ic-cerebellum-temporal_lobe16
 
5.7%
ic-parietal-cerebellum13
 
4.7%
ic-cerebellum_temporal_lob9
 
3.2%
ic-frontal-temporal-cerebellum8
 
2.9%
ic-temporal-cerebellum6
 
2.2%
ic-parietal-minor6
 
2.2%
slight4
 
1.4%
Other values (24)36
 
12.9%

Most occurring characters

ValueCountFrequency (%)
e796
17.7%
l615
13.7%
c471
10.5%
r363
8.1%
-335
7.4%
i301
 
6.7%
m292
 
6.5%
b258
 
5.7%
u215
 
4.8%
a213
 
4.7%
Other values (22)646
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4031
89.5%
Dash Punctuation335
 
7.4%
Connector Punctuation72
 
1.6%
Uppercase Letter45
 
1.0%
Space Separator20
 
0.4%
Other Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e796
19.7%
l615
15.3%
c471
11.7%
r363
9.0%
i301
 
7.5%
m292
 
7.2%
b258
 
6.4%
u215
 
5.3%
a213
 
5.3%
t151
 
3.7%
Other values (11)356
8.8%
Uppercase Letter
ValueCountFrequency (%)
I37
82.2%
C3
 
6.7%
O1
 
2.2%
K1
 
2.2%
L1
 
2.2%
P1
 
2.2%
F1
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-335
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Connector Punctuation
ValueCountFrequency (%)
_72
100.0%
Other Punctuation
ValueCountFrequency (%)
;2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4076
90.5%
Common429
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e796
19.5%
l615
15.1%
c471
11.6%
r363
8.9%
i301
 
7.4%
m292
 
7.2%
b258
 
6.3%
u215
 
5.3%
a213
 
5.2%
t151
 
3.7%
Other values (18)401
9.8%
Common
ValueCountFrequency (%)
-335
78.1%
_72
 
16.8%
20
 
4.7%
;2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e796
17.7%
l615
13.7%
c471
10.5%
r363
8.1%
-335
7.4%
i301
 
6.7%
m292
 
6.5%
b258
 
5.7%
u215
 
4.8%
a213
 
4.7%
Other values (22)646
14.3%

qc_anat_rater_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size64.4 KiB
OK
1003 
fail
108 

Length

Max length4
Median length2
Mean length2.194419442
Min length2

Characters and Unicode

Total characters2,438
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOK
2nd rowOK
3rd rowOK
4th rowOK
5th rowOK

Common Values

ValueCountFrequency (%)
OK1003
90.2%
fail108
 
9.7%
(Missing)1
 
0.1%

Length

2021-06-01T18:43:08.423855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:43:08.577720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ok1003
90.3%
fail108
 
9.7%

Most occurring characters

ValueCountFrequency (%)
O1003
41.1%
K1003
41.1%
f108
 
4.4%
a108
 
4.4%
i108
 
4.4%
l108
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2006
82.3%
Lowercase Letter432
 
17.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f108
25.0%
a108
25.0%
i108
25.0%
l108
25.0%
Uppercase Letter
ValueCountFrequency (%)
O1003
50.0%
K1003
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2438
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O1003
41.1%
K1003
41.1%
f108
 
4.4%
a108
 
4.4%
i108
 
4.4%
l108
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O1003
41.1%
K1003
41.1%
f108
 
4.4%
a108
 
4.4%
i108
 
4.4%
l108
 
4.4%

qc_anat_notes_rater_3
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)6.5%
Missing1004
Missing (%)90.3%
Memory size38.5 KiB
headmotion
62 
noise
18 
half head
11 
no T1, T2
10 
less headmotion
 
5
Other values (2)
 
2

Length

Max length15
Median length10
Mean length9.268518519
Min length5

Characters and Unicode

Total characters1,001
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st rownoise
2nd rownoise
3rd rownoise
4th rowheadmotion
5th rowheadmotion

Common Values

ValueCountFrequency (%)
headmotion62
 
5.6%
noise18
 
1.6%
half head11
 
1.0%
no T1, T210
 
0.9%
less headmotion5
 
0.4%
quality check1
 
0.1%
no anat images1
 
0.1%
(Missing)1004
90.3%

Length

2021-06-01T18:43:08.819890image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:43:08.923053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
headmotion67
45.6%
noise18
 
12.2%
head11
 
7.5%
half11
 
7.5%
no11
 
7.5%
t210
 
6.8%
t110
 
6.8%
less5
 
3.4%
quality1
 
0.7%
images1
 
0.7%
Other values (2)2
 
1.4%

Most occurring characters

ValueCountFrequency (%)
o163
16.3%
e103
10.3%
n97
9.7%
a93
9.3%
h90
9.0%
i87
8.7%
d78
7.8%
t69
6.9%
m68
6.8%
39
 
3.9%
Other values (13)114
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter912
91.1%
Space Separator39
 
3.9%
Uppercase Letter20
 
2.0%
Decimal Number20
 
2.0%
Other Punctuation10
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o163
17.9%
e103
11.3%
n97
10.6%
a93
10.2%
h90
9.9%
i87
9.5%
d78
8.6%
t69
7.6%
m68
7.5%
s29
 
3.2%
Other values (8)35
 
3.8%
Decimal Number
ValueCountFrequency (%)
110
50.0%
210
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Uppercase Letter
ValueCountFrequency (%)
T20
100.0%
Other Punctuation
ValueCountFrequency (%)
,10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin932
93.1%
Common69
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o163
17.5%
e103
11.1%
n97
10.4%
a93
10.0%
h90
9.7%
i87
9.3%
d78
8.4%
t69
7.4%
m68
7.3%
s29
 
3.1%
Other values (9)55
 
5.9%
Common
ValueCountFrequency (%)
39
56.5%
110
 
14.5%
,10
 
14.5%
210
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o163
16.3%
e103
10.3%
n97
9.7%
a93
9.3%
h90
9.0%
i87
8.7%
d78
7.8%
t69
6.9%
m68
6.8%
39
 
3.9%
Other values (13)114
11.4%

qc_func_rater_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size64.3 KiB
OK
1035 
fail
 
76

Length

Max length4
Median length2
Mean length2.136813681
Min length2

Characters and Unicode

Total characters2,374
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfail
2nd rowOK
3rd rowOK
4th rowOK
5th rowOK

Common Values

ValueCountFrequency (%)
OK1035
93.1%
fail76
 
6.8%
(Missing)1
 
0.1%

Length

2021-06-01T18:43:09.200411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:43:09.285748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ok1035
93.2%
fail76
 
6.8%

Most occurring characters

ValueCountFrequency (%)
O1035
43.6%
K1035
43.6%
f76
 
3.2%
a76
 
3.2%
i76
 
3.2%
l76
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2070
87.2%
Lowercase Letter304
 
12.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f76
25.0%
a76
25.0%
i76
25.0%
l76
25.0%
Uppercase Letter
ValueCountFrequency (%)
O1035
50.0%
K1035
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2374
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O1035
43.6%
K1035
43.6%
f76
 
3.2%
a76
 
3.2%
i76
 
3.2%
l76
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O1035
43.6%
K1035
43.6%
f76
 
3.2%
a76
 
3.2%
i76
 
3.2%
l76
 
3.2%

qc_func_notes_rater_3
Categorical

HIGH CORRELATION
MISSING

Distinct47
Distinct (%)61.8%
Missing1036
Missing (%)93.2%
Memory size37.5 KiB
no func images
11 
no T1
11 
ERROR #5
 
3
bad quality
 
2
bad T1
 
2
Other values (42)
47 

Length

Max length29
Median length9
Mean length9.815789474
Min length5

Characters and Unicode

Total characters746
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)48.7%

Sample

1st rowERROR #24
2nd rowERROR #184
3rd rowERROR #46
4th rowno func images
5th rowno func images

Common Values

ValueCountFrequency (%)
no func images11
 
1.0%
no T111
 
1.0%
ERROR #53
 
0.3%
bad quality2
 
0.2%
bad T12
 
0.2%
ERROR #2162
 
0.2%
ERROR #172
 
0.2%
ERROR #462
 
0.2%
ERROR #12
 
0.2%
ERROR #522
 
0.2%
Other values (37)37
 
3.3%
(Missing)1036
93.2%

Length

2021-06-01T18:43:09.520872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
error49
28.8%
no22
12.9%
t113
 
7.6%
func11
 
6.5%
images11
 
6.5%
bad4
 
2.4%
53
 
1.8%
change2
 
1.2%
any2
 
1.2%
462
 
1.2%
Other values (43)51
30.0%

Most occurring characters

ValueCountFrequency (%)
R147
19.7%
94
12.6%
E49
 
6.6%
O49
 
6.6%
#49
 
6.6%
n39
 
5.2%
138
 
5.1%
o26
 
3.5%
a21
 
2.8%
218
 
2.4%
Other values (26)216
29.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter258
34.6%
Lowercase Letter212
28.4%
Decimal Number121
16.2%
Space Separator94
 
12.6%
Other Punctuation61
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n39
18.4%
o26
12.3%
a21
9.9%
e15
 
7.1%
u13
 
6.1%
c13
 
6.1%
i13
 
6.1%
m13
 
6.1%
g13
 
6.1%
f11
 
5.2%
Other values (9)35
16.5%
Decimal Number
ValueCountFrequency (%)
138
31.4%
218
14.9%
811
 
9.1%
511
 
9.1%
610
 
8.3%
49
 
7.4%
09
 
7.4%
77
 
5.8%
35
 
4.1%
93
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
R147
57.0%
E49
 
19.0%
O49
 
19.0%
T13
 
5.0%
Other Punctuation
ValueCountFrequency (%)
#49
80.3%
?12
 
19.7%
Space Separator
ValueCountFrequency (%)
94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin470
63.0%
Common276
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R147
31.3%
E49
 
10.4%
O49
 
10.4%
n39
 
8.3%
o26
 
5.5%
a21
 
4.5%
e15
 
3.2%
u13
 
2.8%
c13
 
2.8%
i13
 
2.8%
Other values (13)85
18.1%
Common
ValueCountFrequency (%)
94
34.1%
#49
17.8%
138
13.8%
218
 
6.5%
?12
 
4.3%
811
 
4.0%
511
 
4.0%
610
 
3.6%
49
 
3.3%
09
 
3.3%
Other values (3)15
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R147
19.7%
94
12.6%
E49
 
6.6%
O49
 
6.6%
#49
 
6.6%
n39
 
5.2%
138
 
5.1%
o26
 
3.5%
a21
 
2.8%
218
 
2.4%
Other values (26)216
29.0%

SUB_IN_SMP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.1 KiB
1
763 
0
349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1,112
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1763
68.6%
0349
31.4%

Length

2021-06-01T18:43:09.723050image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:43:09.794389image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1763
68.6%
0349
31.4%

Most occurring characters

ValueCountFrequency (%)
1763
68.6%
0349
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1112
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1763
68.6%
0349
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common1112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1763
68.6%
0349
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1763
68.6%
0349
31.4%

Correlations

2021-06-01T18:43:10.039358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-01T18:43:12.024440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-01T18:43:13.744740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-01T18:43:15.493984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-01T18:42:25.805435image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-01T18:42:30.885833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-01T18:42:34.375626image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-01T18:42:38.588711image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0Unnamed: 0.1SUB_IDXsubjectSITE_IDFILE_IDDX_GROUPDSM_IV_TRAGE_AT_SCANSEXHANDEDNESS_CATEGORYHANDEDNESS_SCORESFIQVIQPIQFIQ_TEST_TYPEVIQ_TEST_TYPEPIQ_TEST_TYPEADI_R_SOCIAL_TOTAL_AADI_R_VERBAL_TOTAL_BVADI_RRB_TOTAL_CADI_R_ONSET_TOTAL_DADI_R_RSRCH_RELIABLEADOS_MODULEADOS_TOTALADOS_COMMADOS_SOCIALADOS_STEREO_BEHAVADOS_RSRCH_RELIABLEADOS_GOTHAM_SOCAFFECTADOS_GOTHAM_RRBADOS_GOTHAM_TOTALADOS_GOTHAM_SEVERITYSRS_VERSIONSRS_RAW_TOTALSRS_AWARENESSSRS_COGNITIONSRS_COMMUNICATIONSRS_MOTIVATIONSRS_MANNERISMSSCQ_TOTALAQ_TOTALCOMORBIDITYCURRENT_MED_STATUSMEDICATION_NAMEOFF_STIMULANTS_AT_SCANVINELAND_RECEPTIVE_V_SCALEDVINELAND_EXPRESSIVE_V_SCALEDVINELAND_WRITTEN_V_SCALEDVINELAND_COMMUNICATION_STANDARDVINELAND_PERSONAL_V_SCALEDVINELAND_DOMESTIC_V_SCALEDVINELAND_COMMUNITY_V_SCALEDVINELAND_DAILYLVNG_STANDARDVINELAND_INTERPERSONAL_V_SCALEDVINELAND_PLAY_V_SCALEDVINELAND_COPING_V_SCALEDVINELAND_SOCIAL_STANDARDVINELAND_SUM_SCORESVINELAND_ABC_STANDARDVINELAND_INFORMANTWISC_IV_VCIWISC_IV_PRIWISC_IV_WMIWISC_IV_PSIWISC_IV_SIM_SCALEDWISC_IV_VOCAB_SCALEDWISC_IV_INFO_SCALEDWISC_IV_BLK_DSN_SCALEDWISC_IV_PIC_CON_SCALEDWISC_IV_MATRIX_SCALEDWISC_IV_DIGIT_SPAN_SCALEDWISC_IV_LET_NUM_SCALEDWISC_IV_CODING_SCALEDWISC_IV_SYM_SCALEDEYE_STATUS_AT_SCANAGE_AT_MPRAGEBMIanat_cnranat_efcanat_fberanat_fwhmanat_qi1anat_snrfunc_efcfunc_fberfunc_fwhmfunc_dvarsfunc_outlierfunc_qualityfunc_mean_fdfunc_num_fdfunc_perc_fdfunc_gsrqc_rater_1qc_notes_rater_1qc_anat_rater_2qc_anat_notes_rater_2qc_func_rater_2qc_func_notes_rater_2qc_anat_rater_3qc_anat_notes_rater_3qc_func_rater_3qc_func_notes_rater_3SUB_IN_SMP
00150002150002PITTno_filename11.016.771AmbiNaN103.0116.089.0WASIWASIWASI16.09.05.04.01.04.012.04.08.03.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN10.2015391.19466416.2234583.8780000.15271112.0724520.61312845.4465511.8733391.0549310.0006410.0114430.1168288.03.9801000.054346failNaNOKNaNfailic-parietal-cerebellumOKNaNfailERROR #241
11250003250003PITTPitt_005000311.024.451RNaN124.0128.0115.0WASIWASIWASI27.022.05.03.01.04.013.05.08.01.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1FluoxetineNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN7.1657011.12675210.4600084.2822380.1617169.2411550.57830156.2863502.0121120.9498570.0004740.0317810.322092135.067.1641790.041862OKNaNOKNaNOKNaNOKNaNOKNaN1
22350004350004PITTPitt_005000411.019.091RNaN113.0108.0117.0WASIWASIWASI19.012.05.03.01.04.018.06.012.02.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN7.6981441.2262189.7257503.8816840.1741869.3234630.57896063.3179431.8661041.1806050.0082620.0142600.12774529.014.4278610.046745OKNaNOKNaNOKNaNOKNaNOKNaN1
33450005450005PITTPitt_005000511.013.732RNaN119.0117.0118.0WASIWASIWASI23.019.03.04.01.04.012.04.08.01.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1FluoxetineNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN9.0718071.25627811.1982263.6286670.11926910.8142000.55606470.8003541.9182781.0920300.0017110.0192050.12813622.010.9452740.027963OKNaNOKNaNmaybeic-parietal-cerebellumOKNaNOKNaN0
44550006550006PITTPitt_005000611.013.371LNaN109.099.0119.0WASIWASIWASI13.010.04.03.01.04.012.04.08.04.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN8.0267981.4071666.2820553.6745390.13064710.1235740.56294275.3646792.2138731.0868300.0015000.0069190.0701433.01.4925370.054006OKNaNOKNaNmaybeic-parietal slightOKNaNOKNaN1
55650007650007PITTPitt_005000711.017.781RNaN110.0106.0112.0WASIWASIWASI21.014.09.01.01.03.017.05.012.02.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN5.7758971.1612316.9910934.1047070.2362147.6387460.60755464.6821091.8345211.0993800.0028050.0282840.15124638.018.9054730.055615OKNaNOKNaNmaybeic-cerebellum_temporal_lobOKNaNOKNaN1
66750008750008PITTPitt_005000811.032.451RNaN123.0123.0114.0WASIWASIWASI24.020.010.02.01.0NaN16.04.012.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1SertralineNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN6.6696861.2414917.9459373.8314620.1317338.5079720.57144760.6820831.9096221.0383690.0005240.0180280.16927558.028.8557210.056363OKNaNOKNaNOKNaNOKNaNOKNaN1
77850009850009PITTPitt_005000911.033.861RNaN126.0118.0128.0WASIWASIWASI20.011.03.02.01.0NaN10.04.06.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1PantoprazoleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN4.4479481.2013117.8580953.6221180.1977926.5522310.59659364.4274312.0325041.1801880.0031650.0105130.09197614.06.9651740.053051OKNaNOKNaNfailic-parietal-cerebellumOKNaNOKNaN1
88950010950010PITTPitt_005001011.035.201LNaN81.081.093.0WASIWASIWASINaNNaNNaNNaN1.0NaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN11.4503880.93166829.2338113.8523560.07377613.2921400.49136084.9248492.0219971.1163370.0011260.0064940.0725457.03.4825870.036569OKNaNOKNaNmaybeic-cerebellum_temporal_lobOKNaNOKNaN1
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